Journal articles on the topic 'Evolutionary problem support system'
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Zhang, Xiao Xia, and Yun Yong Ma. "A Decision Support System with EDA_PR Algorithm for the Hot Rolling Scheduling." Advanced Materials Research 756-759 (September 2013): 4466–70. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.4466.
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This paper presents a hybrid algorithm for the hot rolling scheduling problem, which is derived from the actual steel production, and some features make the solution methodology more difficult. The hybrid strategy is based on the solution construction mechanism of estimation of distribution algorithm (EDA) with path relinking (PR), an evolutionary method, which results in a novel approach that we call EDA_PR. Moreover, a decision support system in which the algorithm has been embedded for the hot rolling scheduling is designed. The computational experiments show that the EDA_PR method has more potential for improvement to solve the hot rolling scheduling problem compared with the manual scheduling method.
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PARMEE, I. C. "Improving problem definition through interactive evolutionary computation." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 16, no. 3 (June 2002): 185–202. http://dx.doi.org/10.1017/s0890060402163050.
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Poor definition and uncertainty are primary characteristics of conceptual design processes. During the initial stages of these generally human-centric activities, little knowledge pertaining to the problem at hand may be available. The degree of problem definition will depend on information available in terms of appropriate variables, constraints, and both quantitative and qualitative objectives. Typically, the problem space develops with information gained in a dynamical process in which design optimization plays a secondary role, following the establishment of a sufficiently well-defined problem domain. This paper concentrates on background human–computer interaction relating to the machine-based generation of high-quality design information that, when presented in an appropriate manner to the designer, supports a better understanding of a problem domain. Knowledge gained from such information combined with the experiential knowledge of the designer can result in a reformulation of the problem, providing increased definition and greater confidence in the machine-based representation. Conceptual design domains related to gas turbine blade cooling systems and a preliminary air frame configuration are introduced. These are utilized to illustrate the integration of interactive evolutionary strategies that support the extraction of optimal design information, its presentation to the designer, and subsequent human-based modification of the design domain based on knowledge gained from the information received. An experimental iterative designer or evolutionary search process resulting in a better understanding of the problem and improved machine-based representation of the design domain is thus established.
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Galdiero, Enzo, Francesco De Paola, Nicola Fontana, Maurizio Giugni, and Dragan Savic. "Decision support system for the optimal design of district metered areas." Journal of Hydroinformatics 18, no. 1 (January 21, 2015): 49–61. http://dx.doi.org/10.2166/hydro.2015.023.
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The establishment of district metered areas (DMAs) is widely recognized as one of the most effective techniques for the optimal management of water distribution networks (WDNs). However, its implementation in real cases is a very challenging task that requires decision aiding. In this work, a comprehensive methodology for the optimal design of DMAs is presented and discussed. The proposed approach consists of a two-objectives optimization problem subjected to a number of constraints related to the topology of the network, financial issues and the network hydraulics. A multi-objective evolutionary algorithm (MOEA) is combined with tools from graph theory for the solution of the problem. The validation of the model and the calibration of the parameters are performed through the application to a well-known example from the literature. Low cost as compared to the budget and a good saving in leakage are obtained.
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ALAMANIOTIS, MILTIADIS, ANDREAS IKONOMOPOULOS, and LEFTERI H. TSOUKALAS. "OPTIMAL ASSEMBLY OF SUPPORT VECTOR REGRESSORS WITH APPLICATION TO SYSTEM MONITORING." International Journal on Artificial Intelligence Tools 21, no. 06 (December 2012): 1250034. http://dx.doi.org/10.1142/s0218213012500340.
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Power plants are high complexity systems running risks of low frequency but high consequence. The field of machine learning appears to offer the necessary tools for developing automated instrument surveillance systems supporting decision-making in critical systems such as power stations. A novel prediction method is presented with the aim to enhance system safety and performance by making an ahead-of-time prediction of the status of fundamental system components and subsequent detection of abnormalities. The utilization of a linear assembly of support vector regressors employing unique kernels is proposed in a hybrid computational scheme that encompasses the formulation of a multi-objective optimization problem addressed with an evolutionary algorithm that employs Pareto theory to identify an optimal solution. The approach is tested on the ahead of time prediction of the crack length in power plant turbine blades utilizing historical data. The results obtained highlight the efficiency of the proposed methodology since better performance over the standalone support vector regressors is observed.
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Wazirali, Raniyah A., Arwa D. Alzughaibi, and Zenon Chaczko. "Adaptation of Evolutionary Algorithms for Decision Making on Building Construction Engineering (TSP Problem)." International Journal of Electronics and Telecommunications 60, no. 1 (March 1, 2014): 113–16. http://dx.doi.org/10.2478/eletel-2014-0015.
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Abstract The report revolve on building construction engineering and management, in which there are a lot of requirements such as well supervision and accuracy and being in position to forecast uncertainties that may arise and mechanisms to solve them. It also focuses on the way the building and construction can minimise the cost of building and wastages of materials. The project will be based of heuristic methods of Artificial Intelligence (AI). There are various evolution methods, but report focus on two experiments Pattern Recognition and Travelling Salesman Problem (TSP). The Pattern Recognition focuses Evolutionary Support Vector Machine Inference System for Construction Management. The construction is very dynamic are has a lot of uncertainties, no exact data this implies that the inference should change according to the environment so that it can fit the reality, therefore there a need of Support Vector Machine Inference System to solve these problems. TSP focus on reducing cost of building construction engineering and also reduces material wastages, through its principals of finding the minimum cost path of the salesman.
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Nivethitha, V., and P. M Abhinaya. "Combinatorics based problem specific software architecture formulation using multi-objective genetic algorithm." International Journal of Engineering & Technology 7, no. 1.7 (February 5, 2018): 79. http://dx.doi.org/10.14419/ijet.v7i1.7.9579.
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In Software Development Process, the design of complex systems is an important phase where software architects have to deal with abstract artefacts, procedures and ideas to discover the most suitable underlying architecture. Due to uncontrolled modifications of the design and frequent change of requirements, many of the working systems do not have a proper architecture. Most of the approaches recover the architectural blocks at the end of the development process which are not appropriate to the system considered. In order to structure these systems software components compositions and interactions should be properly adjusted which is a tedious work. Search-based Software Engineering (SBSE) is an emerging area which can support the decision making process of formulating the software architecture from initial analysis models. Thus component-based architectures is articulated as a multiple optimisation problem using evolutionary algorithms. Totally different metrics is applied looking on the design needs and also the specific domain. Thus during this analysis work, an effort has been created to propose a multi objective evolutionary approach for the invention of the underlying software system architectures beside a versatile encoding structure, correct style metrics for the fitness operate to enhance the standard and accuracy of the software system design.
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Farahani, Elham Darmanaki, and Jafar Habibi. "Configuration Management Model in Evolutionary Software Product Line." International Journal of Software Engineering and Knowledge Engineering 26, no. 03 (April 2016): 433–55. http://dx.doi.org/10.1142/s0218194016500182.
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In Software Product Line (SPL), Configuration Management (CM) is a multi-dimensional problem. On the one hand, the Core Assets that constitute a configuration need to be managed, and on the other hand, each product in the product line that is built using a configuration must be managed, and furthermore, the management of all these configurations must be coordinated under a single process. Therefore, CM for product lines is more complex than for single systems. The CM of any software system involves four closely related activities: Change Management (ChM), Version Management (VM), System Building (SB) and Release Management (RM) [I. Sommerville, Software Engineering, 9th edn. (Addison-Wesley, 2010)]. The aim of this paper is to provide ChM and VM models for evolutionary-based SPL system development and maintenance. The proposed models support any level of aggregation in SPLs and have been applied to Mobile SPL as a case study.
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Senatore, Rosa, Antonio Della Cioppa, and Angelo Marcelli. "Automatic Diagnosis of Neurodegenerative Diseases: An Evolutionary Approach for Facing the Interpretability Problem." Information 10, no. 1 (January 17, 2019): 30. http://dx.doi.org/10.3390/info10010030.
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Background: The use of Artificial Intelligence (AI) systems for automatic diagnoses is increasingly in the clinical field, being a useful support for the identification of several diseases. Nonetheless, the acceptance of AI-based diagnoses by the physicians is hampered by the black-box approach implemented by most performing systems, which do not clearly state the classification rules adopted. Methods: In this framework we propose a classification method based on a Cartesian Genetic Programming (CGP) approach, which allows for the automatic identification of the presence of the disease, and concurrently, provides the explicit classification model used by the system. Results: The proposed approach has been evaluated on the publicly available HandPD dataset, which contains handwriting samples drawn by Parkinson’s disease patients and healthy controls. We show that our approach compares favorably with state-of-the-art methods, and more importantly, allows the physician to identify an explicit model relevant for the diagnosis based on the most informative subset of features. Conclusion: The obtained results suggest that the proposed approach is particularly appealing in that, starting from the explicit model, it allows the physicians to derive a set of guidelines for defining novel testing protocols and intervention strategies.
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Xue, Xingsi, and Jianhua Liu. "Optimizing Ontology Alignment Through Compact MOEA/D." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 04 (February 2, 2017): 1759004. http://dx.doi.org/10.1142/s0218001417590042.
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In order to support semantic inter-operability in many domains through disparate ontologies, we need to identify correspondences between the entities across different ontologies, which is commonly known as ontology matching. One of the challenges in ontology matching domain is how to select weights and thresholds in the ontology aligning process to aggregate the various similarity measures to obtain a satisfactory alignment, so called ontology meta-matching problem. Nowadays, the most suitable methodology to address the ontology meta-matching problem is through Evolutionary Algorithm (EA), and the Multi-Objective Evolutionary Algorithms (MOEA) based approaches are emerging as a new efficient methodology to face the meta-matching problem. Moreover, for dynamic applications, it is necessary to perform the system self-tuning process at runtime, and thus, efficiency of the configuration search strategies becomes critical. To this end, in this paper, we propose a problem-specific compact Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), in the whole ontology matching process of ontology meta-matching system, to optimize the ontology alignment. The experimental results show that our proposal is able to highly reduce the execution time and main memory consumption of determining the optimal alignments through MOEA/D based approach by 58.96% and 67.60% on average, respectively, and the quality of the alignments obtained is better than the state of the art ontology matching systems.
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Белых, М. А., В. Ф. Барабанов, С. Л. Подвальный, and А. К. Донских. "STRUCTURE OF THE INTELLIGENT SYSTEM FOR SUPPORTING EVOLUTIONARY ALGORITHMS." ВЕСТНИК ВОРОНЕЖСКОГО ГОСУДАРСТВЕННОГО ТЕХНИЧЕСКОГО УНИВЕРСИТЕТА, no. 3 (July 2, 2021): 7–13. http://dx.doi.org/10.36622/vstu.2021.17.3.001.
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Производится краткий обзор эволюционных алгоритмов как методов поиска и оптимизации при моделировании различных процессов и управлении сложными объектами. Основным критерием рассмотрения алгоритмов является практическая эффективность в решении оптимизационных задач, в частности, задачи поиска оптимального маршрута. В качестве алгоритмов, перспективно подходящих для внедрения в структуру интеллектуальной системы поддержки эволюционных алгоритмов, рассматриваются генетический алгоритм, алгоритм муравьиной колонии и алгоритм пчелиной колонии, отмечены их преимущества и недостатки. Oсуществлен краткий обзор программных средств, работающих на базе эволюционных алгоритмов, с указанием их сильных и слабых сторон, в частности, их ориентированность на определенный алгоритм. Pазработана структурная схема интеллектуальной системы поддержки эволюционных алгоритмов, которая обладает универсальностью и не привязана к конкретному алгоритму. Интеллектуальная система состоит из совокупности модулей: интерфейсный модуль, модуль работы с документами, модуль математического ядра поддержки ЭА, модуль настроек, модуль формирования целевой функции, модуль справочной системы, графический модуль. Приведено описание функционирования каждого из них. Система позволяет осуществить выбор оптимального решения, варьируя параметры и используя инструменты, предоставленные системой или заданные пользователем We give a brief review of evolutionary algorithms as search and optimization methods for modeling various processes and managing complex objects. The main criterion for considering algorithms is practical efficiency in solving optimization problems, in particular, the problem of finding the optimal route. We considered the genetic algorithm, the ant colony algorithm and the bee colony algorithm as algorithms that are promisingly suitable for introducing into the structure of an intelligent system for supporting evolutionary algorithms, we noted their advantages and disadvantages. We carried out a brief overview of software tools based on evolutionary algorithms, with an indication of their strengths and weaknesses, in particular, their focus on a specific algorithm. We developed a structural diagram of an intelligent system for supporting evolutionary algorithms, which is universal and not tied to a specific algorithm. The intelligent system consists of a set of modules: an interface module, a module for working with documents, a module for the mathematical core of EA support, a settings module, a module for generating an objective function, a help system module, a graphic module. We give a description of the functioning of each of them. The system allows one to select the optimal solution by varying the parameters and using tools provided by the system or specified by the user
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Zhou, Xiumin, Gongxuan Zhang, Tian Wang, Mingyue Zhang, Xiji Wang, and Wei Zhang. "Makespan–Cost–Reliability-Optimized Workflow Scheduling Using Evolutionary Techniques in Clouds." Journal of Circuits, Systems and Computers 29, no. 10 (December 30, 2019): 2050167. http://dx.doi.org/10.1142/s0218126620501674.
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Most popular scientific workflow systems can now support the deployment of tasks to the cloud. The execution of workflow on cloud has become a multi-objective scheduling in order to meet the needs of users in many aspects. Cost and makespan are considered to be the two most important objects. In addition to these, there are some other Quality-of-Service (QoS) parameters including system reliability, energy consumption and so on. Here, we focus on three objectives: cost, makespan and system reliability. In this paper, we propose a Multi-objective Evolutionary Algorithm on the Cloud (MEAC). In the algorithm, we design some novel schemes including problem-specific encoding and also evolutionary operations, such as crossover and mutation. Simulations on real-world and random workflows are conducted and the results show that MEAC can get on average about 5% higher hypervolume value than some other workflow scheduling algorithms.
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Tait, A. "Genetic exchange and evolutionary relationships in protozoan and helminth parasites." Parasitology 100, S1 (June 1990): S75—S87. http://dx.doi.org/10.1017/s0031182000073030.
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SummaryThe study of genetic exchange systems and the use of genetic analysis has been relatively limited in parasites leading to considerable gaps in our basic knowledge. This lack of knowledge makes it difficult to draw firm conclusions as to how these systems evolved. An additional problem is also raised by the difficulties in defining evolutionary distances particularly with the unicellular protozoa, using classical ultrastructural and cytological criteria. While these difficulties have by no means been overcome, the use of rapid sequencing techniques applied to the ribosomal genes has allowed measurement of evolutionary distances, and considerable advances in our understanding of the genetic exchange systems in a few parasitic protozoa have recently been made. The conclusions from these recent sets of analyses are reviewed and then examined together in order to discuss the evolution of genetic exchange systems in parasitic protozoa. The evolutionary distances defined by ribosome sequence analysis show that parasites are an extremely divergent group, with distances which, in some cases, are orders of magnitude greater than the distances between mammals and fish; furthermore these studies suggest that the parasitic protozoa or their free-living ancestors are extremely ancient. These findings support the view that parasitism has occurred independently many times and that the parasitic life-style has been adopted by evolutionarily distinct groups. The recent observation of a non-obligatory genetic exchange system in the diploid but evolutionarily ancient kinetoplastid Trypanosoma brucei suggests that diploidy and meiosis are extremely old. The observation, in parasitic protozoa and helminths, that selfing or non-obligatory mating is a common feature suggests that these processes may be strategies to overcome the cost of meiosis. In this context, the question of what selective forces maintain genetic exchange is discussed.
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Vinogradova, O. L., E. S. Tomilovskaya, and I. B. Kozlovskaya. "GRAVITATIONAL FACTOR AS A BASE OF THE EVOLUTIONARY ADAPTATION OF ANIMAL ORGANISMS TO ACTIVITIES IN THEEARTH CONDITIONS." Aerospace and Environmental Medicine 54, no. 6 (2020): 5–26. http://dx.doi.org/10.21687/0233-528x-2020-54-6-5-26.
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A review of the currently available ideas about the role of gravitational factor in the activity of the sensorimotor and cardiovascular systems, as well as new fundamental problems and questions for space medicine and physiology, is presented. The review presents data on the embryogenesis of animals under conditions of weightlessness, the evolution of the motor and cardiovascular systems and the peculiarities of their functioning under conditions of gravity, as well as in the change of gravitational load. Much attention is paid to the results of unique studies in modeling gravitational unloading on Earth: antiorthostatic hypokinesia, dry immersion and suspension, which made it possible to study the mechanisms of regulation of various body systems under conditions of altered gravity. Terrestrial organisms have learned to function in the gravitational field. Almost all systems of their body are gravitationally dependent. However, the extent and mechanisms of this dependence have long remained unclear. Space flights have opened up the possibility of studying the activity of living systems in the absence of gravity. Among the factors mediating the effect of weightlessness on the motor system, changes in the activity of sensory systems occupy an important place. Under the Earth conditions, the afferent support of motion control systems is polyreceptive: this is vision, and the vestibular apparatus, supporting and muscular afferentations. In zero gravity, the activity of some channels is completely eliminated (support afferentation), others are distorted (vestibular apparatus), and still others are weakened (proprioception). Similar processes occur in the cardiovascular system: with the loss of the pressure gradient caused by gravity, profound changes occur in the structure and functioning of the heart and vessels, both resistive and capacitive. The question of how much the various changes occurring in the cardiovascular system are associated with the disappearance of the gravitationally dependent pressure gradient is still open. It is not possible to solve all the problems of gravitational physiology In space flights. Therefore, various methods have been developed for simulating gravitational unloading on Earth. New data on the mechanisms of changes occurring in the sensorimotor system were obtained by comparing flight data and data obtained in model experiments. The fundamental problem for the gravitational physiology of cardiovascular system is the degree of correspondence of the changes observed in laboratory animals and under model conditions (antiorthostatic hypokinesia, immersion, suspension) with the changes that are recorded in real space flight in humans. This problem is specially discussed in the review. At the same time, in the light of the upcoming interplanetary expeditions, many questions remain unresolved, in particular, the problems of post-flight readaptation of the motor and cardiovascular systems to gravity conditions. This is a fight against loss of strength, endurance, orthostatic instability. The development and improvement of a system for preventing the negative effects of space flight factors is impossible without understanding the mechanisms of development of the observed changes.
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Branke, Juergen, and Christoph W. Pickardt. "Evolutionary search for difficult problem instances to support the design of job shop dispatching rules." European Journal of Operational Research 212, no. 1 (July 2011): 22–32. http://dx.doi.org/10.1016/j.ejor.2011.01.044.
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Ahmed, Faisal, Mohammad Shahadat Hossain, Raihan Ul Islam, and Karl Andersson. "An Evolutionary Belief Rule-Based Clinical Decision Support System to Predict COVID-19 Severity under Uncertainty." Applied Sciences 11, no. 13 (June 23, 2021): 5810. http://dx.doi.org/10.3390/app11135810.
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Accurate and rapid identification of the severe and non-severe COVID-19 patients is necessary for reducing the risk of overloading the hospitals, effective hospital resource utilization, and minimizing the mortality rate in the pandemic. A conjunctive belief rule-based clinical decision support system is proposed in this paper to identify critical and non-critical COVID-19 patients in hospitals using only three blood test markers. The experts’ knowledge of COVID-19 is encoded in the form of belief rules in the proposed method. To fine-tune the initial belief rules provided by COVID-19 experts using the real patient’s data, a modified differential evolution algorithm that can solve the constraint optimization problem of the belief rule base is also proposed in this paper. Several experiments are performed using 485 COVID-19 patients’ data to evaluate the effectiveness of the proposed system. Experimental result shows that, after optimization, the conjunctive belief rule-based system achieved the accuracy, sensitivity, and specificity of 0.954, 0.923, and 0.959, respectively, while for disjunctive belief rule base, they are 0.927, 0.769, and 0.948. Moreover, with a 98.85% AUC value, our proposed method shows superior performance than the four traditional machine learning algorithms: LR, SVM, DT, and ANN. All these results validate the effectiveness of our proposed method. The proposed system will help the hospital authorities to identify severe and non-severe COVID-19 patients and adopt optimal treatment plans in pandemic situations.
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Li, Yahui, and Yang Li. "Two-Step Many-Objective Optimal Power Flow Based on Knee Point-Driven Evolutionary Algorithm." Processes 6, no. 12 (December 4, 2018): 250. http://dx.doi.org/10.3390/pr6120250.
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To coordinate the economy, security and environment protection in the power system operation, a two-step many-objective optimal power flow (MaOPF) solution method is proposed. In step 1, it is the first time that knee point-driven evolutionary algorithm (KnEA) is introduced to address the MaOPF problem, and thereby the Pareto-optimal solutions can be obtained. In step 2, an integrated decision analysis technique is utilized to provide decision makers with decision supports by combining fuzzy c-means (FCM) clustering and grey relational projection (GRP) method together. In this way, the best compromise solutions (BCSs) that represent decision makers’ different, even conflicting, preferences can be automatically determined from the set of Pareto-optimal solutions. The primary contribution of the proposal is the innovative application of many-objective optimization together with decision analysis for addressing MaOPF problems. Through examining the two-step method via the IEEE 118-bus system and the real-world Hebei provincial power system, it is verified that our approach is suitable for addressing the MaOPF problem of power systems.
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Zhang, Yanshuai, and S. Thomas Ng. "AN ANT COLONY SYSTEM BASED DECISION SUPPORT SYSTEM FOR CONSTRUCTION TIME-COST OPTIMIZATION." Journal of Civil Engineering and Management 18, no. 4 (September 11, 2012): 580–89. http://dx.doi.org/10.3846/13923730.2012.704164.
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Time and cost are the two most important factors to be considered in every construction project. In order to maximize the profit, both the client and contractor would strive to minimize the project duration and cost concurrently. In the past, most of the research studies related to construction time and cost assumed time to be constant, leaving the analyses based purely on a single objective of cost. Acknowledging this limitation, an evolutionary-based optimization algorithm known as an ant colony system is applied in this study to solve the multi-objective time-cost optimization problems. In this paper, a model is developed using Visual Basic for Application™ which is integrated with Microsoft Project™. Through a test study, the performance of the proposed model is compared against other analytical methods previously used for time-cost modeling. The results show that the model based on the ant colony system techniques can generate better solutions without utilizing excessive computational resources. The model, therefore, provides an efficient means to support planners and managers in making better time-cost decisions efficiently.
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Eichhoff, Julian R., and Dieter Roller. "A survey on automating configuration and parameterization in evolutionary design exploration." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 29, no. 4 (October 7, 2015): 333–50. http://dx.doi.org/10.1017/s0890060415000372.
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AbstractConfiguration and parameterization of optimization frameworks for the computational support of design exploration can become an exclusive barrier for the adoption of such systems by engineers. This work addresses the problem of defining the elements that constitute a multiple-objective design optimization problem, that is, design variables, constants, objective functions, and constraint functions. In light of this, contributions are reviewed from the field of evolutionary design optimization with respect to their concrete implementation for design exploration. Machine learning and natural language processing are supposed to facilitate feasible approaches to the support of configuration and parameterization. Hence, the authors further review promising machine learning and natural language processing methods for automatic knowledge elicitation and formalization with respect to their implementation for evolutionary design optimization. These methods come from the fields of product attribute extraction, clustering of design solutions, relationship discovery, computation of objective functions, metamodeling, and design pattern extraction.
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Lesinski, Eugene, and Steven Corns. "A Pareto Based Multi-Objective Evolutionary Algorithm Approach to Military Installation Rail Infrastructure Investment." Industrial and Systems Engineering Review 7, no. 2 (December 30, 2019): 64–75. http://dx.doi.org/10.37266/iser.2019v7i2.pp64-75.
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Decision making for military railyard infrastructure is an inherently multi-objective problem, balancing cost versus capability. In this research, a Pareto-based Multi-Objective Evolutionary Algorithm is compared to a military rail inventory and decision support tool (RAILER). The problem is formulated as a multi-objective evolutionary algorithm in which the overall railyard condition is increased while decreasing cost to repair and maintain. A prioritization scheme for track maintenance is introduced that takes into account the volume of materials transported over the track and each rail segment’s primary purpose. Available repair options include repairing current 90 gauge rail, upgrade of rail segments to 115 gauge rail, and the swapping of rail removed during the upgrade. The proposed Multi-Objective Evolutionary Algorithm approach provides several advantages to the RAILER approach. The MOEA methodology allows decision makers to incorporate additional repair options beyond the current repair or do nothing options. It was found that many of the solutions identified by the evolutionary algorithm were both lower cost and provide a higher overall condition that those generated by DoD’s rail inventory and decision support system, RAILER. Additionally, the MOEA methodology generates lower cost, higher capability solutions when reduced sets of repair options are considered. The collection of non-dominated solutions provided by this technique gives decision makers increased flexibility and the ability to evaluate whether an additional cost repair solution is worth the increase in facility rail condition.
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Srinivasan, Sujatha, and Sivakumar Ramakrishnan. "A hybrid agent based virtual organization for studying knowledge evolution in social systems." Artificial Intelligence Research 1, no. 2 (September 24, 2012): 99. http://dx.doi.org/10.5430/air.v1n2p99.
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Social modeling applies computational methods and techniques to the analysis of social processes and human behavior.Cultural algorithms (CA’s) are evolutionary systems which utilize agent technology and which supports any evolutionarystrategy like genetic algorithm, evolutionary algorithm or swarm intelligence or ant algorithms. CA’s have been used formodeling the evolution of complex social systems, for re-engineering rule based systems, for data mining, and for solvingoptimization problems. In the current study a cultural algorithm framework is used to model an Agent Based VirtualOrganization (ABVO) for studying the dynamics of a social system at micro as well as macro level. Research gap exists indefining a concrete and systematic method for evaluating and validating Agent Based Social Systems (ABSS). Also theknowledge evolution process at micro and macro levels of an organization needs further exploration. The proposed CA isapplied to the problem of multi-objective optimization (MOO) of classification rules. The evolutionary knowledgeproduced by the agents in creating the rules is accepted into the belief space of the CA and macro evolution takes place.The belief space in turn influences the agents in successive generations. The rules created by the individuals and theknowledge sources created during evolution provide a concrete method to evaluate both the individuals as well as thewhole social system. The feasibility of the system has been tested on bench mark data sets and the results are encouraging.
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Long, Kim C., William S. Duff, John W. Labadie, Mitchell J. Stansloski, Walajabad S. Sampath, and Edwin K. P. Chong. "Multi-objective fatigue life optimization using Tabu Genetic Algorithms." International Journal of Structural Integrity 6, no. 6 (December 7, 2015): 677–88. http://dx.doi.org/10.1108/ijsi-12-2014-0066.
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Purpose – The purpose of this paper is to present a real world application of an innovative hybrid system reliability optimization algorithm combining Tabu search with an evolutionary algorithm (TSEA). This algorithm combines Tabu search and Genetic algorithm to provide a more efficient search method. Design/methodology/approach – The new algorithm is applied to an aircraft structure to optimize its reliability and maintain its structural integrity. For retrofitting the horizontal stabilizer under severe stall buffet conditions, a decision support system (DSS) is developed using the TSEA algorithm. This system solves a reliability optimization problem under cost and configuration constraints. The DSS contains three components: a graphical user interface, a database and several modules to provide the optimized retrofitting solutions. Findings – The authors found that the proposed algorithm performs much better than state-of-the-art methods such as Strength Pareto Evolutionary Algorithms on bench mark problems. In addition, the proposed TSEA method can be easily applied to complex real world optimization problem with superior performance. When the full combination of all input variables increases exponentially, the DSS become very efficient. Practical implications – This paper presents an application of the TSEA algorithm for solving nonlinear multi-objective reliability optimization problems embedded in a DSS. The solutions include where to install doublers and stiffeners. Compromise programming is used to rank all non-dominant solutions. Originality/value – The proposed hybrid algorithm (TSEA) assigns fitness based upon global dominance which ensures its convergence to the non-dominant front. The high efficiency of this algorithm came from using Tabu list to guidance the search to the Pareto-optimal solutions.
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Koenig, Reinhard, Yufan Miao, Anna Aichinger, Katja Knecht, and Kateryna Konieva. "Integrating urban analysis, generative design, and evolutionary optimization for solving urban design problems." Environment and Planning B: Urban Analytics and City Science 47, no. 6 (December 18, 2019): 997–1013. http://dx.doi.org/10.1177/2399808319894986.
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To better support urban designers in planning sustainable, resilient, and livable urban environments, new methods and tools are needed. A variety of computational approaches have been proposed, including different forms of spatial analysis to evaluate the performance of design proposals, or the automated generation of urban design proposals based on specific parameters. However, most of these propositions have produced separate tools and disconnected workflows. In the context of urban design optimization procedures, one of the main challenges of integrating urban analytics and generative methods is a suitable computational representation of the urban design problem. To overcome this difficulty, we present a holistic data representation for urban fabrics, including the layout of street networks, parcels, and buildings, which can be used efficiently with evolutionary optimization algorithms. We demonstrate the use of the data structure implemented for the software Grasshopper for Rhino3D as part of a flexible, modular, and extensible optimization system that can be used for a variety of urban design problems and is able to reconcile potentially contradicting design goals in a semi-automated design process. The proposed optimization system aims to assist a designer by populating the design space with options for more detailed exploration. We demonstrate the functionality of our system using the example of an urban master-design project for the city of Weimar.
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Aranda-Corral, Gonzalo A., Miguel A. Rodríguez, Iñaki Fernández de Viana, and María Isabel G. Arenas. "Genetic Hybrid Optimization of a Real Bike Sharing System." Mathematics 9, no. 18 (September 10, 2021): 2227. http://dx.doi.org/10.3390/math9182227.
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In recent years there has been a growing interest in resource sharing systems as one of the possible ways to support sustainability. The use of resource pools, where people can drop a resource to be used by others in a local context, is highly dependent on the distribution of those resources on a map or graph. The optimization of these systems is an NP-Hard problem given its combinatorial nature and the inherent computational load required to simulate the use of a system. Furthermore, it is difficult to determine system overhead or unused resources without building the real system and test it in real conditions. Nevertheless, algorithms based on a candidate solution allow measuring hypothetical situations without the inconvenience of a physical implementation. In particular, this work focuses on obtaining the past usage of bike loan network infrastructures to optimize the station’s capacity distribution. Bike sharing systems are a good model for resource sharing systems since they contain common characteristics, such as capacity, distance, and temporary restrictions, which are present in most geographically distributed resources systems. To achieve this target, we propose a new approach based on evolutionary algorithms whose evaluation function will consider the cost of non-used bike places as well as the additional kilometers users would have to travel in the new distribution. To estimate its value, we will consider the geographical proximity and the trend in the areas to infer the behavior of users. This approach, which improves user satisfaction considering the past usage of the former infrastructure, as far as we know, has not been applied to this type of problem and can be generalized to other resource sharing problems with usage data.
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Leitão, António, Adriano Vinhas, Penousal Machado, and Francisco Câmara Pereira. "A Genetic Algorithms Approach for Inverse Shortest Path Length Problems." International Journal of Natural Computing Research 4, no. 4 (October 2014): 36–54. http://dx.doi.org/10.4018/ijncr.2014100103.
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Inverse Combinatorial Optimization has become a relevant research subject over the past decades. In graph theory, the Inverse Shortest Path Length problem becomes relevant when people don't have access to the real cost of the arcs and want to infer their value so that the system has a specific outcome, such as one or more shortest paths between nodes. Several approaches have been proposed to tackle this problem, relying on different methods, and several applications have been suggested. This study explores an innovative evolutionary approach relying on a genetic algorithm. Two scenarios and corresponding representations are presented and experiments are conducted to test how they react to different graph characteristics and parameters. Their behaviour and differences are thoroughly discussed. The outcome supports that evolutionary algorithms may be a viable venue to tackle Inverse Shortest Path problems.
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Hu, Zhi-Hua, Chen Wei, and Xiao-Kun Yu. "Apparel distribution with uncertain try-on time by evolutionary algorithm." International Journal of Clothing Science and Technology 27, no. 1 (March 2, 2015): 75–90. http://dx.doi.org/10.1108/ijcst-08-2013-0099.
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Purpose – The purpose of this paper is to study the problem of a routing problem with uncertain try-on service time (VRPUS) for apparel distribution, and to devise solution strategies coping with the uncertainty by an evolutionary algorithm. VRPUS belongs to the category of practical routing models integrated with uncertain service times. However, in the background of apparel distribution, it has distinct features. The try-on service will improve the customer satisfaction by providing experiences to customers; the return cost is saved; the customer loyalty is improved for experiencing face-to-face try-on services. However, the uncertainty of try-on service time makes the apparel distribution process uncertain and incurs additional risk management cost, such that the logistics companies should optimally make decisions on the choice of the service and the service processes. Design/methodology/approach – This paper devised a mixed-integer programming (MIP) model for the base vehicle routing problem (VRP) and then it is extended to support the solution strategies for uncertain try-on times. A try-on time estimation parameter and a time reservation parameter are used to cope with the uncertain try-on time, and the try-on rejection strategy is applied when the uncertain try-on time is realized at customer and no surplus time can be used for try-on service besides distributing to remainder customers. Due to the computational complexity of VRPUS, an evolutionary algorithm is designed for solving it. These parameters and strategy options are designed for the operational decisions by logistics companies. Finally, a decision support system (DSS) is designed. Findings – Five experimental scenarios are performed to reveal the impacts of parameters and solution strategies coping with uncertain try-on time on the distribution cost, return cost, and the try-on service failure. The tuning methods are designed to assist the decisions by logistics companies. Originality/value – A new routing problem is addressed for apparel distribution in fashion industry especially in the context of booming apparel e-commerce, which is a VRP with uncertain try-on service time for apparel distribution; three strategies are developed to cope with the try-on time uncertainty. The proposed method is also a theoretical base for designing a practical DSS for logistics companies to provide try-on service to customers.
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Siqueira, Lucas, and Sandra Venske. "Ab Initio Protein Structure Prediction Using Evolutionary Approach: A Survey." Revista de Informática Teórica e Aplicada 28, no. 2 (August 29, 2021): 11–24. http://dx.doi.org/10.22456/2175-2745.111993.
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Protein Structure Prediction (PSP) problem is to determine the three-dimensional structure of a protein only from its primary structure. Misfolding of a protein causes human diseases. Thus, the knowledge of the structure and functionality of proteins, combined with the prediction of their structure is a complex problem and a challenge for the area of computational biology. The metaheuristic optimization algorithms are naturally applicable to support in solving NP-hard problems.These algorithms are bio-inspired, since they were designed based on procedures found in nature, such as the successful evolutionary behavior of natural systems. In this paper, we present a survey on methods to approach the \textit{ab initio} protein structure prediction based on evolutionary computing algorithms, considering both single and multi-objective optimization. An overview of the works is presented, with some details about which characteristics of the problem are considered, as well as specific points of the algorithms used. A comparison between the approaches is presented and some directions of the research field are pointed out.
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Nhlabatsi, Armstrong, Bashar Nuseibeh, and Yijun Yu. "Security Requirements Engineering for Evolving Software Systems." International Journal of Secure Software Engineering 1, no. 1 (January 2010): 54–73. http://dx.doi.org/10.4018/jsse.2010102004.
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Long-lived software systems often undergo evolution over an extended period. Evolution of these systems is inevitable as they need to continue to satisfy changing business needs, new regulations and standards, and introduction of novel technologies. Such evolution may involve changes that add, remove, or modify features; or that migrate the system from one operating platform to another. These changes may result in requirements that were satisfied in a previous release of a system not being satisfied in subsequent versions. When evolutionary changes violate security requirements, a system may be left vulnerable to attacks. In this article we review current approaches to security requirements engineering and conclude that they lack explicit support for managing the effects of software evolution. We then suggest that a cross fertilization of the areas of software evolution and security engineering would address the problem of maintaining compliance to security requirements of software systems as they evolve.
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Kozlov, V. V., T. V. Tomashevska, and N. I. Kuznetsov. "Using of Optimization Models in Financial Decision Support Systems." Statistics of Ukraine 88, no. 1 (May 1, 2020): 75–83. http://dx.doi.org/10.31767/su.1(88)2020.01.09.
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The article discusses the use of optimization models in financial decision support systems (DSS). The architecture of the DSS is considered. It is determined by the nature of the interaction of its main components: the user interface, database and data warehouse, documents and rules, models and analytical tools, communications infrastructure and networks, as well as elements of these parts. The architecture of the DSS for solving problems of financial management is determined by the nature of interaction of its main components.
Conceptual and functional models are presented. The functional model of the DSS reflects the structure of control actions on decision-makers, persons involved in the implementation of corrective actions necessary for effective financial performance. According to the functional model, a block diagram of the DSS is proposed. The block diagram of decision support consists of three main subsystems and provides modular-block construction. The proposed system is characterized by an open architecture and can be easily modified for functional expansion or for connecting and using external databases. The DSS should provide a common operating environment for modeling objects in a dynamically changing economic situation. The operating environment contains basic calculation algorithms, and allows the user to create own algorithms for calculating cash flows and used indicators. Thus, the key element of the integrated decision support system for managing the financial condition of the enterprise is the models laid down in the basis of the system.
The architecture of the DSS based on algorithms is considered. A DSS of this type contains a set of algorithms for solving a selected class of problems. Of the factors that influence the choice of a specific architecture of the DSS in financial management, we can highlight (i) the need for further development of the system, (ii) its adaptation, and (iii) the application of the evolutionary approach to the development of the DSS. The components of the system are: data input component, resource allocation component, strategy selection component, output component. The resource allocation component contains the following sub-components: determination of dependence coefficients, solution of optimization problem, solution of equation. The strategy selection component includes the sub-components for calculating the values of integral design characteristics, for calculating the values of groups of design characteristics, and for the pairwise comparison of strategies.
The information model of the component “The distribution of financial resources of the enterprise” is presented. The DSS performs data processing and checks for critical decision characteristics. The formalized structure of the algorithm for using the proposed models of distribution of financial resources, types of model integration are considered.
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Yang, Hsiao-Fang, and Heng-Li Yang. "Development of a self-design system for greeting cards on the basis of interactive evolutionary computation." Kybernetes 45, no. 3 (March 7, 2016): 521–35. http://dx.doi.org/10.1108/k-07-2015-0178.
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Purpose – User-centered product designs have been attracting increasing attention, particularly in digital design. In interacting with the design support system, designers may face problems such as changing demands (e.g. unclear demands) and insufficient descriptions of these demands (e.g. data scarcity). The purpose of this paper is to build a design support system prototype for demonstrating the feasibility of meeting the high involvement of users in digital products. Design/methodology/approach – Interactive evolutionary computation is applied. Findings – A prototype of self-design greeting card system (SDGCS) was proposed. It provides professional design layouts, offers users numerous self-design models, and allows nonprofessional users to easily design greeting cards. The results of this study show that users were satisfied with the functionality, usefulness, and ease-of-use of the SDGCS. Research limitations/implications – This study used digital card design as an example for demonstrating the feasibility of satisfying the unclear needs of uses, enabling users to design a digital card creatively and complete their designs quickly. However, the current system only supports the design of static objects and layout of card. And the evaluation sample size was small, which might affect generalizability of the findings. Practical implications – In practice, greeting card web operators can image the feasible business models by providing the attraction of self-design functionalities. Originality/value – In current human-centric marketing era, consumers have begun to request interaction with designers in creating the value of a product. However, very few previous studies have provided support for digital product self-design. This study demonstrated the feasibility of satisfying the needs of self-design.
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Parmee, Ian C., Dragan Cvetković, Andrew H. Watson, and Christopher R. Bonham. "Multiobjective Satisfaction within an Interactive Evolutionary Design Environment." Evolutionary Computation 8, no. 2 (June 2000): 197–222. http://dx.doi.org/10.1162/106365600568176.
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The paper introduces the concept of an Interactive Evolutionary Design System (IEDS) that supports the engineering designer during the conceptual/preliminary stages of the design process. Requirement during these early stages relates primarily to design search and exploration across a poorly defined space as the designer's knowledge base concerning the problem area develops. Multiobjective satisfaction plays a major role, and objectives are likely to be ill-defined and their relative importance uncertain. Interactive evolutionary search and exploration provides information to the design team that contributes directly to their overall understanding of the problem domain in terms of relevant objectives, constraints, and variable ranges. This paper describes the development of certain elements within an interactive evolutionary conceptual design environment that allows off-line processing of such information leading to a redefinition of the design space. Such redefinition may refer to the inclusion or removal of objectives, changes concerning their relative importance, or the reduction of variable ranges as a better understanding of objective sensitivity is established. The emphasis, therefore, moves from a multiobjective optimization over a preset number of generations to a relatively continuous interactive evolutionary search that results in the optimal definition of both the variable and objective space relating to the design problem at hand. The paper describes those elements of the IEDS relating to such multiobjective information gathering and subsequent design space redefinition.
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Aleksandrova, E. K., and D. A. Shakin. "System of forming information and legal worlview of the cadets in a military university." Pedagogicheskiy Zhurnal Bashkortostana 92, no. 2 (2021): 110–23. http://dx.doi.org/10.21510/1817-3292-2021-92-2-110-123.
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The article designs the system to form informational and legal worldview of cadets of the institutes of the National Guard of the Russian Federation (those who are trained in specialty 40.05.01. "Legal support of national security"). The main principles of the designed system are: protopostulates (they form the basis of the hierarchy of the main provisions of the designed system and represent those principles of philosophy that determine and control the course of research in pedagogical science); metapostulates (represent supportive assumptions for educational science); postulates (set the structure of the subject of study and the rules that must be followed in the process of solving the problem). As one of the main postulates of the system, the authors give the provision on the presence of two levels of information and legal worldview. The informational and legal worldview of the first level is interpreted as a subspecies of the professional worldview, and the informational and legal worldview of the second level is interpreted as a subspecies of the professional worldview. The implementation of the system is carried out on the basis of the application of the cognitive-evolutionary approach. The cognitive-evolutionary approach used in pedagogical research for the first time and it is based on the use of the cognitive-evolutionary theory of Piaget in the educational process of a military university. An example of the practical implementation of the system for the formation of an informational and legal worldview when conducting legal cycle lessons for cadets of higher educational institutions of the Russian National Guard troops is given. A feature of the described pedagogical experiment is the use of a cognitive-evolutionary approach in the educational process of a military university, based on the use of scaffolding technology. The application of scaffolding technology is carried out in an organic unity with the case method. The use of the case method, on the one hand, is a reflection of the currently actively developing plot-game paradigm related to topical problems of general and applied ontology, and on the other hand, it organically combines with the idea of identifying three phases in the process of forming an information and legal worldview - phase of prevailing accommodation, phase of choice of a mixed adaptive strategy and phase of prevailing assimilation.
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Ahmadi, S. M., H. Kebriaei, and H. Moradi. "Constrained coverage path planning: evolutionary and classical approaches." Robotica 36, no. 6 (February 21, 2018): 904–24. http://dx.doi.org/10.1017/s0263574718000139.
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SUMMARYThe constrained coverage path planning addressed in this paper refers to finding an optimal path traversed by a unmanned aerial vehicle (UAV) to maximize its coverage on a designated area, considering the time limit and the feasibility of the path. The UAV starts from its current position to assess the condition of a new entry to the area. Nevertheless, the UAV needs to comply with the coverage task, simultaneously and therefore, it is likely that the optimal policy would not be the shortest path in such a condition, since a wider area can be covered through a longer path. From the other side, along with a longer path, the UAV may not reach to the target in due time. In addition, the speed of UAV is assumed to be constant and as a result, a feasible path needs to be smooth enough to support this assumption. The problem is modeled as an Epsilon-constraint optimization in which a coverage function has to be maximized, considering the constraints on the length and the smoothness of the path. For this purpose, a new genetic path planning algorithm with adaptive operator selection is proposed to solve such a complicated constrained optimization problem. The proposed approach has been compared to some classical approaches like, a modified version of the Artificial Potential Field and a modified version of Dijkstra's algorithm (a graph-based approach). All the methods are implemented and tested in different scenarios and their performances are evaluated via the simulation results.
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JIN, XIDONG, and ROBERT G. REYNOLDS. "USING KNOWLEDGE-BASED SYSTEMS WITH HIERARCHICAL ARCHITECTURES TO GUIDE EVOLUTIONARY SEARCH." International Journal on Artificial Intelligence Tools 09, no. 01 (March 2000): 27–44. http://dx.doi.org/10.1142/s0218213000000045.
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Regional Knowledge is useful in identifying patterns of relationships between variables, and it is particularly important in solving constrained global optimization problems. However, regional knowledge is generally unavailable prior to the optimization search. The questions here are: 1) Is it possible for an evolutionary system to learn regional knowledge during the search instead of having to acquire it beforehand? and 2) How can this regional knowledge be used to expedite evolutionary search? This paper defines regional schemata to provide an explicit mechanism to support the acquisition, storage and manipulation of regional knowledge. In a Cultural Algorithm framework, the belief space "contains" a set of these regional schemata, arranged in a hierarchical architecture, to enable the knowledge-based evolutionary system to learn regional knowledge during the search and apply the learned knowledge to guide the search. This mechanism can be used to guide the optimization search in a direct way, by "pruning" the infeasible regions and "promoting" the promising regions. Engineering problems with nonlinear constraints are tested and the results are discussed. It shows that the proposed mechanism is potential to solve complicated non-linear constrained optimization problems, and some other hard problems, e.g. the optimization problems with "ridges" in landscapes.
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Berardi, L., O. Giustolisi, Z. Kapelan, and D. A. Savic. "Development of pipe deterioration models for water distribution systems using EPR." Journal of Hydroinformatics 10, no. 2 (March 1, 2008): 113–26. http://dx.doi.org/10.2166/hydro.2008.012.
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The economic and social costs of pipe failures in water and wastewater systems are increasing, putting pressure on utility managers to develop annual replacement plans for critical pipes that balance investment with expected benefits in a risk-based management context. In addition to the need for a strategy for solving such a multi-objective problem, analysts and water system managers need reliable and robust failure models for assessing network performance. In particular, they are interested in assessing a conduit's propensity to fail and how to assign criticality to an individual pipe segment. In this paper, pipe deterioration is modelled using Evolutionary Polynomial Regression. This data-driven technique yields symbolic formulae that are intuitive and easily understandable by practitioners. The case study involves a water quality zone within a distribution system and entails the collection of historical data to develop network performance indicators. Finally, an approach for incorporating such indicators into a decision support system for pipe rehabilitation/replacement planning is introduced and articulated.
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PARMEE, I. C. "Evolutionary and adaptive strategies for efficient search across whole system engineering design hierarchies." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 12, no. 5 (November 1998): 431–45. http://dx.doi.org/10.1017/s0890060498125039.
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Evolutionary and Adaptive strategies (ES & AS) for diverse multilevel search across a preliminary, whole-system design hierarchy defined by discrete and continuous variable parameters are described. Such strategies provide high-level decision support when integrated with preliminary design software describing the major elements of an engineering system. Initial work involving a Structured Genetic Algorithm (stGA) with appropriate mutation regimes to encourage search diversity is described and preliminary results are presented. The shortcomings of the stGA approach are identified and alternative strategies are introduced. A dual agent strategy (GAANT) involving elements of an ant colony search and an evolutionary search concurrently manipulating the discrete and continuous variable parameter sets is presented. Appropriate communication between the two search agents results in a more efficient search across the hierarchy than that achieved by the stGA, while also simplifying the chromosomal representation. This simplification allows the further development of the preliminary design hierarchy in terms of complexity. The technique therefore represents a significant contribution to configuration design where multilevel, mixed discrete/continuous parameter design problems can be prevalent.
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Szlapczynski, Rafal. "Evolutionary Planning of Safe Ship Tracks in Restricted Visibility." Journal of Navigation 68, no. 1 (September 26, 2014): 39–51. http://dx.doi.org/10.1017/s0373463314000587.
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The paper presents the continuation of the author's research on ship track planning by means of Evolutionary Algorithms (EA). The presented method uses EA to search for an optimal set of safe tracks for all ships involved in an encounter. Until now the method assumed good visibility – compliance with standard rules of the Convention on the International Regulations for Preventing Collisions at Sea (COLREGS, 1972). However, in restricted visibility, when Rule 19 applies instead of Rules 11 to 18, the problem is a different one. Therefore this paper introduces the extended method, with a focus on compliance with Rule 19 and its implications. It includes descriptions of detecting, penalizing and eliminating violations of Rule 19. The method has been implemented and the paper contains sample results of computer simulation tests carried out for ship encounters in restricted visibility in both open and restricted waters. They confirm the effectiveness of the chosen approach and suggest that the method could be applied in on board decision support systems.
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Lezama, Fernando, Ricardo Faia, Pedro Faria, and Zita Vale. "Demand Response of Residential Houses Equipped with PV-Battery Systems: An Application Study Using Evolutionary Algorithms." Energies 13, no. 10 (May 14, 2020): 2466. http://dx.doi.org/10.3390/en13102466.
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Households equipped with distributed energy resources, such as storage units and renewables, open the possibility of self-consumption of on-site generation, sell energy to the grid, or do both according to the context of operation. In this paper, a model for optimizing the energy resources of households by an energy service provider is developed. We consider houses equipped with technologies that support the actual reduction of energy bills and therefore perform demand response actions. A mathematical formulation is developed to obtain the optimal scheduling of household devices that minimizes energy bill and demand response curtailment actions. In addition to the scheduling model, the innovative approach in this paper includes evolutionary algorithms used to solve the problem under two optimization approaches: (a) the non-parallel approach combine the variables of all households at once; (b) the parallel-based approach takes advantage of the independence of variables between households using a multi-population mechanism and independent optimizations. Results show that the parallel-based approach can improve the performance of the tested evolutionary algorithms for larger instances of the problem. Thus, while increasing the size of the problem, namely increasing the number of households, the proposed methodology will be more advantageous. Overall, vortex search overcomes all other tested algorithms (including the well-known differential evolution and particle swarm optimization) achieving around 30% better fitness value in all the cases, demonstrating its effectiveness in solving the proposed problem.
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Singh, Pawan Kumar, Supratim Das, Ram Sarkar, and Mita Nasipuri. "Feature Selection Using Harmony Search for Script Identification from Handwritten Document Images." Journal of Intelligent Systems 27, no. 3 (July 26, 2018): 465–88. http://dx.doi.org/10.1515/jisys-2016-0070.
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Abstract The feature selection process can be considered a problem of global combinatorial optimization in machine learning, which reduces the irrelevant, noisy, and non-contributing features, resulting in acceptable classification accuracy. Harmony search algorithm (HSA) is an evolutionary algorithm that is applied to various optimization problems such as scheduling, text summarization, water distribution networks, vehicle routing, etc. This paper presents a hybrid approach based on support vector machine and HSA for wrapper feature subset selection. This approach is used to select an optimized set of features from an initial set of features obtained by applying Modified log-Gabor filters on prepartitioned rectangular blocks of handwritten document images written in either of 12 official Indic scripts. The assessment justifies the need of feature selection for handwritten script identification where local and global features are computed without knowing the exact importance of features. The proposed approach is also compared with four well-known evolutionary algorithms, namely genetic algorithm, particle swarm optimization, tabu search, ant colony optimization, and two statistical feature dimensionality reduction techniques, namely greedy attribute search and principal component analysis. The acquired results show that the optimal set of features selected using HSA gives better accuracy in handwritten script recognition.
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Hou, Caiping, and Xiyu Liu. "Tissue-like P system based DNA-GA for clustering." TELKOMNIKA Indonesian Journal of Electrical Engineering 16, no. 3 (December 1, 2015): 565. http://dx.doi.org/10.11591/tijee.v16i3.1649.
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In recent years, DNA GA algorithm is drawing attention from scholars. The algorithm combines the DNA encoding and Genetic Algorithm, which solve the premature convergence of genetic algorithms, the weak local search capability and binary Hamming cliff problems effectively.How to design a more effective way to improve the performance of DNA-GA algorithm is more worth studying. As is known to all,the tissue-like P system can search for the optimal clustering partition with the help of its parallel computing advantage effectivel. This paper is under this premise and presents DNA-GA algorithm based on tissue-like P systems (TPDNA-GA) with a loop structure of cells, which aims to combine the parallelism and the evolutionary rules of tissue-like P systems to improve performance of the DNA-GA algorithm. The objective of this paper is to use the TPDNA-GA algorithm to support clustering in order to find the best clustering center.This algorithm is of particular interest to when dealing with large and heterogeneous data sets and when being faced with an unknown number of clusters. Experimental results show that the proposed TPDNA-GA algorithm for clustering is superior or competitive to classical k-means algorithm and several evolutionary clustering algorithms.
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Deepika, N., and O. S. Abdul Qadir. "A Study on Nature Inspired Task Scheduling Algorithms in Cloud Environment." Asian Journal of Computer Science and Technology 8, S2 (March 5, 2019): 79–82. http://dx.doi.org/10.51983/ajcst-2019.8.s2.2019.
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Cloud computing is an encouraging paradigm which offers resources to customers on their demand with least cost. Task scheduling is the key difficult in cloud computing which decreases the performance of the system. To develop performance of the system, there is necessity of an effective task-scheduling algorithm. Nature inspired computing is a technique that is inspired by practices detected from nature. These computing techniques led to the growth of algorithms called Nature Inspired Algorithms (NIA). These algorithms are theme of computational intelligence. The persistence of raising such algorithms is to enhance engineering problems. Nature inspired algorithms have enlarged huge popularity in recent years to challenge hard real world (NP hard and NP complete) problems and resolve complex optimization functions whose actual solution doesn’t occur. The paper presents a complete review of 12 nature inspired algorithms. This study offers the researchers with a single platform to analyze the conventional and contemporary nature inspired algorithms in terms of essential input parameters, their key evolutionary strategies and application areas. This study would support the research community to recognize what all algorithms could be observed for big scale global optimization to overwhelm the problem of ‘curse of dimensionality’.
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Papanikolaou, Michail, Emanuele Pagone, Konstantinos Georgarakis, Keith Rogers, Mark Jolly, and Konstantinos Salonitis. "Design Optimisation of the Feeding System of a Novel Counter-Gravity Casting Process." Metals 8, no. 10 (October 11, 2018): 817. http://dx.doi.org/10.3390/met8100817.
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The appropriate design of feeders in a rigging system is critical for ensuring efficient compensation for solidification shrinkage, thus eliminating (shrinkage-related) porosity and contributing to the production of superior quality castings. In this study, a multi-objective optimisation framework combined with Computational Fluid Dynamics (CFD) simulations has been introduced to investigate the effect of the feeders’ geometry on shrinkage porosity aiming to optimise casting quality and yield for a novel counter-gravity casting process (CRIMSON). The weighted sum technique was employed to convert this multi-objective optimisation problem to a single objective one. Moreover, an evolutionary multi-objective optimisation algorithm (NSGA-II) has been applied to estimate the trade-off between the objective functions and support decision makers on selecting the optimum solution based on the desired properties of the final casting product and the process characteristics. This study is one of the first attempts to combine CFD simulations with multi-objective optimisation techniques in counter-gravity casting. The obtained results indicate the benefits of applying multi-objective optimisation techniques to casting processes.
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Jiang, Jian, and Fen Zhang. "Detecting Portable Executable Malware by Binary Code Using an Artificial Evolutionary Fuzzy LSTM Immune System." Security and Communication Networks 2021 (July 7, 2021): 1–12. http://dx.doi.org/10.1155/2021/3578695.
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As the planet watches in shock the evolution of the COVID-19 pandemic, new forms of sophisticated, versatile, and extremely difficult-to-detect malware expose society and especially the global economy. Machine learning techniques are posing an increasingly important role in the field of malware identification and analysis. However, due to the complexity of the problem, the training of intelligent systems proves to be insufficient in recognizing advanced cyberthreats. The biggest challenge in information systems security using machine learning methods is to understand the polymorphism and metamorphism mechanisms used by malware developers and how to effectively address them. This work presents an innovative Artificial Evolutionary Fuzzy LSTM Immune System which, by using a heuristic machine learning method that combines evolutionary intelligence, Long-Short-Term Memory (LSTM), and fuzzy knowledge, proves to be able to adequately protect modern information system from Portable Executable Malware. The main innovation in the technical implementation of the proposed approach is the fact that the machine learning system can only be trained from raw bytes of an executable file to determine if the file is malicious. The performance of the proposed system was tested on a sophisticated dataset of high complexity, which emerged after extensive research on PE malware that offered us a realistic representation of their operating states. The high accuracy of the developed model significantly supports the validity of the proposed method. The final evaluation was carried out with in-depth comparisons to corresponding machine learning algorithms and it has revealed the superiority of the proposed immune system.
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Ramakurthi, Veera Babu, V. K. Manupati, José Machado, and Leonilde Varela. "A Hybrid Multi-Objective Evolutionary Algorithm-Based Semantic Foundation for Sustainable Distributed Manufacturing Systems." Applied Sciences 11, no. 14 (July 8, 2021): 6314. http://dx.doi.org/10.3390/app11146314.
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Rising energy prices, increasing maintenance costs, and strict environmental regimes have augmented the already existing pressure on the contemporary manufacturing environment. Although the decentralization of supply chain has led to rapid advancements in manufacturing systems, finding an efficient supplier simultaneously from the pool of available ones as per customer requirement and enhancing the process planning and scheduling functions are the predominant approaches still needed to be addressed. Therefore, this paper aims to address this issue by considering a set of gear manufacturing industries located across India as a case study. An integrated classifier-assisted evolutionary multi-objective evolutionary approach is proposed for solving the objectives of makespan, energy consumption, and increased service utilization rate, interoperability, and reliability. To execute the approach initially, text-mining-based supervised machine-learning models, namely Decision Tree, Naïve Bayes, Random Forest, and Support Vector Machines (SVM) were adopted for the classification of suppliers into task-specific suppliers. Following this, with the identified suppliers as input, the problem was formulated as a multi-objective Mixed-Integer Linear Programming (MILP) model. We then proposed a Hybrid Multi-Objective Moth Flame Optimization algorithm (HMFO) to optimize process planning and scheduling functions. Numerical experiments have been carried out with the formulated problem for 10 different instances, along with a comparison of the results with a Non-Dominated Sorting Genetic Algorithm (NSGA-II) to illustrate the feasibility of the approach.
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Ekong, Victor. "SOFT COMPUTING SYSTEM FOR THE DIAGNOSIS OF HORMONAL IMBALANCE." Transactions on Machine Learning and Artificial Intelligence 7, no. 6 (January 8, 2020): 30–42. http://dx.doi.org/10.14738/tmlai.76.7507.
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Soft computing, as a science of modelling systems, applies techniques such as evolutionary computing, fuzzy logic, and their hybrids to solve real life problems. Soft computing techniques are quite tolerant to incomplete, imprecise, and uncertainty when dealing with complex situations. This study adopts a hybrid of genetic algorithm and fuzzy logic in diagnosing hormonal imbalance. Hormones are chemical messengers that are vital for growth, reproduction, and are essential for human existence. Hormones may sometimes not be balanced which is a medical condition that often go unnoticed and it’s quite difficult to be diagnosed by medical experts. Hormonal imbalance has several symptoms that could also be confused for other ailments. This proposed system serves as support for medical experts to improve the precision of diagnosis of hormonal imbalance. The study further demonstrates the effective hybridization of genetic algorithm and fuzzy logic in resolving human problems.
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Chattaraj, Suvendu, and Abhik Mukherjee. "Utilizing Time Redundancy for Particle Filter-Based Transfer Alignment." Fluctuation and Noise Letters 15, no. 04 (September 29, 2016): 1650024. http://dx.doi.org/10.1142/s0219477516500243.
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Signal detection in the presence of high noise is a challenge in natural sciences. From understanding signals emanating out of deep space probes to signals in protein interactions for systems biology, domain specific innovations are needed. The present work is in the domain of transfer alignment (TA), which deals with estimation of the misalignment of deliverable daughter munitions with respect to that of the delivering mother platform. In this domain, the design of noise filtering scheme has to consider a time varying and nonlinear system dynamics at play. The accuracy of conventional particle filter formulation suffers due to deviations from modeled system dynamics. An evolutionary particle filter can overcome this problem by evolving multiple system models through few support points per particle. However, this variant has even higher time complexity for real-time execution. As a result, measurement update gets deferred and the estimation accuracy is compromised. By running these filter algorithms on multiple processors, the execution time can be reduced, to allow frequent measurement updates. Such scheme ensures better system identification so that performance improves in case of simultaneous ejection of multiple daughters and also results in better convergence of TA algorithms for single daughter.
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Oda, Ryo. "Refusal of Killing a Stranger to Save Five Brothers: How Are Others’ Judgments Anticipated and Favored in a Moral Dilemma Situation?" Letters on Evolutionary Behavioral Science 4, no. 2 (September 2, 2013): 9–12. http://dx.doi.org/10.5178/lebs.2013.26.
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One evolutionary theory of morality, examined here, is based on theories of kin selection while another has proposed that moral judgment is based on a Kantian rule-based system. Using the Trolley Problem, Kurzban et al. (2012) asked subjects to decide whether they would kill one person to save five others, varying the relationship of the subject with the others involved. They revealed that nearly half of the subjects reported that they would be unwilling to push one stranger to his/her death to save five brothers in a footbridge version of the Trolley Problem. In the present study, I tried to replicate this somewhat surprising result in Japanese participants, to investigate the robustness of the finding. I also examined how participants anticipated and favored the moral judgment of other people. If a Kantian decision was made according to the coordination system suggested by Kurzban et al. (2012), a Kantian decision, rather than a Hamiltonian decision, would be anticipated and favored as the decision of people generally. The results seem to support the discussion of Kurzban et al. (2012), that the computational system that delivers Kantian moral judgment functions to coordinate condemnation decisions.
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SORNETTE, DIDIER, SPENCER WHEATLEY, and PETER CAUWELS. "THE FAIR REWARD PROBLEM: THE ILLUSION OF SUCCESS AND HOW TO SOLVE IT." Advances in Complex Systems 22, no. 03 (May 2019): 1950005. http://dx.doi.org/10.1142/s021952591950005x.
Full textAbstract:
Humanity has been fascinated by the pursuit of fortune since time immemorial, and many successful outcomes benefit from strokes of luck. But success is subject to complexity, uncertainty, and change — and at times becoming increasingly unequally distributed. This leads to tension and confusion over to what extent people actually get what they deserve (i.e. fairness/meritocracy). Moreover, in many fields, humans are overconfident and pervasively confuse luck for skill (I win, it is skill; I lose, it is bad luck). In some fields, there is too much risk-taking; in others, not enough. Where success derives in large part from luck — and especially where bailouts skew the incentives (heads, I win; tails, you lose) — it follows that luck is rewarded too much. This incentivizes a culture of gambling, while downplaying the importance of productive effort. And, short-term success is often rewarded, irrespective, and potentially at the detriment, of the long-term system fitness. However, much success is truly meritocratic, and the problem is to discern and reward based on merit. We call this the fair reward problem. To address this, we propose three different measures to assess merit: (i) raw outcome; (ii) risk-adjusted outcome, and (iii) prospective. We emphasize the need, in many cases, for the deductive prospective approach, which considers the potential of a system to adapt and mutate in novel futures. This is formalized within an evolutionary system, comprised of five processes, inter alia handling the exploration–exploitation trade-off. Several human endeavors — including finance, politics, and science — are analyzed through these lenses, and concrete solutions are proposed to support a prosperous and meritocratic society.
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Mishler, Brent D., and Efraín De Luna. "Sistemática filogenética y el concepto de especie." Botanical Sciences, no. 60 (May 2, 2017): 45. http://dx.doi.org/10.17129/botsci.1518.
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There actually is no species problem per se in systematics. Rather, there is a taxon problem. This review argues that a decision must first be made about what entities taxon names are to represent in general. Then, species taxa should be the same kind of things, but just the least inclusive taxa that are named using the formal Linnaean nomenclatorial system. Formal classifications are meant to serve purposes of communication, data storage, predictivity, and function in theories. These purposes are best served by naming only phylogenetically natural, monophyletic groups. These are groups containing all and only descendants of a common ancestor. A phylogenetic species concept can thus be defined, based on a generalized view of the meaning of monophyly and synapomorphy. In the process of building a classification , taxonomic groups are recognized on the basis of clear support for their existence as monophyletic cross-sections of a lineage. Also, formal taxa are named considering their utility in developing and discussing evolutionary process theories.
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Proshkina, Samira Ilgarovna. "The effectiveness of web advertising, its legal regulation and the problem of information systems security." Век информации (сетевое издание) 4, no. 3(12) (June 1, 2020): 1–15. http://dx.doi.org/10.33941/age-info.com43(12)1.
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The work is devoted to an urgent problem — the study of the evolutionary dynamics of web advertising, its assessment and effectiveness, as well as the problem of legal support and security of information systems. The goal is a systematic analysis of web advertising in an unsafe information field, its relevance and criteria for assessing marketing efforts, minimizing risks, maximizing additional profits and image. Research hypothesis — the effectiveness of web advertising is determined by the form of advertising, place of display, location of the block, model of calculation of the advertising campaign. An approach based on the establishment of preferences, partnership between the state and business structures is emphasized. It takes into account the COVID-19 pandemic, a slowdown in the pace and features of the evolution of business companies in self-isolation. The subtasks of influence on the advertising efficiency of the site’s features and web advertising are highlighted. A comprehensive analysis of information and logical security and computational models of web advertising companies was also carried out.
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Wacewicz, Sławomir, Przemysław Żywiczyński, and Sylwester Orzechowski. "Visible movements of the orofacial area." Gesture 15, no. 2 (July 8, 2016): 250–82. http://dx.doi.org/10.1075/gest.15.2.05wac.
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The age-old debate between the proponents of the gesture-first and speech-first positions has returned to occupy a central place in current language evolution theorizing. The gestural scenarios, suffering from the problem known as “modality transition” (why a gestural system would have changed into a predominantly spoken system), frequently appeal to the gestures of the orofacial area as a platform for this putative transition. Here, we review currently available evidence on the significance of the orofacial area in language evolution. While our review offers some support for orofacial movements as an evolutionary “bridge” between manual gesture and speech, we see the evidence as far more consistent with a multimodal approach. We also suggest that, more generally, the “gestural versus spoken” formulation is limiting and would be better expressed in terms of the relative input and interplay of the visual and vocal-auditory sensory modalities.