Relevant bibliographies by topics / Evolutionary problem support system

Academic literature on the topic 'Evolutionary problem support system'

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Published: 4 June 2021
Last updated: 5 February 2022

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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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Dissertations / Theses on the topic "Evolutionary problem support system"

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Cameron, Mark A., and Mark Cameron@csiro au. "A Problem Model for Decision Support Systems." The Australian National University. Faculty of Engineering and Information Technology, 2000. http://thesis.anu.edu.au./public/adt-ANU20020717.144031.

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This body of research focuses on supporting problem-stakeholders, decision-makers and problem-solvers faced with an ill-defined and complex real world problem. An ill-defined problem has a characteristic trait of continual refinement. That is, the definition of the problem changes throughout the problem investigation and resolution process. The central theme of this research is that a support system should provide problem stakeholders with a problem definition model for constructing and manipulating a representation of the definition of the problem as they understand it. The approach adopted herein is to first develop a problem definition model for ill-defined problems— the 6-Component problem definition model. With this model, it is then possible to move on to identifying the types of changes or modifications to the problem definition that problem stakeholders, decision makers and problem solvers may wish to explore. Importantly, there must be a connection between the surface representation of the problem and the underlying implementation of the support system. This research argues that by focusing the support system around the problem definition, it is possible to reduce the mismatch between the problem objectives and the representation of the problem that the support system offers. This research uses the Unified Modelling Language to record and explore the requirements that problem stakeholders, faced with an evolving problem definition, place on a support system. The 6-Component problem definition model is then embedded within a design for an evolutionary support system. This embedding, supported by collaboration diagrams, shows how a system using the 6-Component problem definition model will support stakeholders in their exploration, evaluation and resolution of an ill-defined and complex real-world problem. A case study provides validation of the effectiveness of the 6-Component problem definition model proposed and developed in this work. The case study uses the 6-Component problem definition model as a basis for implementing the Integration Workbench, an evolutionary support system for land-use planning. Stakeholders explore, communicate, evaluate and resolve the Tasmanian Regional Forest Agreement problem with assistance from the Integration Workbench.
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Hasan, S. M. Kamrul Engineering &amp Information Technology Australian Defence Force Academy UNSW. "Evolutionary algorithms for solving job-shop scheduling problems in the presence of process interruptions." Awarded by:University of New South Wales - Australian Defence Force Academy. Engineering & Information Technology, 2009. http://handle.unsw.edu.au/1959.4/43768.

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In this thesis, the Job Shop Scheduling Problem (JSSP) is the problem of interest. The classical JSSP is well-known as an NP-hard problem. Although with current computational capabilities, the small problems are solvable using deterministic methods, it is out of reach when they are larger in size. The complexity of JSSP is further increased when process interruptions, such as machine breakdown and/or machine unavailability, are introduced. Over the last few decades, several stochastic algorithms have been proposed to solve JSSPs. However, none of them are suitable for all kinds of problems. Genetic and Memetic algorithms have proved their effectiveness in these regards, because of their diverse searching behavior. In this thesis, we have developed one genetic algorithm and three different Memetic Algorithms (MAs) for solving JSSPs. Three priority rules are designed, namely partial re-ordering, gap reduction and restricted swapping, and these have been used as local search techniques in designing our MAs. We have solved 40 well-known benchmark problems and compared the results obtained with some of the established algorithms available in the literature. Our algorithm clearly outperforms those established algorithms. For better justification of the superiority of MAs over GA, we have performed statistical significance testing (Student's t-test). The experimental results show that MA, as compared to GA, not only significantly improves the quality of solutions, but also reduces the overall computation. We have extended our work by proposing an improved local search technique, shifted gap-reduction (SGR), which improves the performance of MAs when tested with the relatively difficult test problems. We have also modified the new algorithm to accommodate JSSPs with machine unavailability and also developed a new reactive scheduling technique to re-optimize the schedule after machine breakdowns. We have considered two scenarios of machine unavailability. Firstly, where the unavailability information is available in advance (predictive), and secondly, where the information is known after a real breakdown (reactive). We show that the revised schedule is mostly able to recover if the interruptions occur during the early stages of the schedules. We also confirm that the effect of a single continuous breakdown has more impact compared to short multiple breakdowns, even if the total durations of the breakdowns are the same. Finally, for convenience of implementation, we have developed a decision support system (DSS). In the DSS, we have built a graphical user interface (GUI) for user friendly data inputs, model choices, and output generation. This DSS tool will help users in solving JSSPs without understanding the complexity of the problem and solution approaches, as well as will contribute in reducing the computational and operational costs.
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Kojima, Kazuaki, and Kazuhisa Miwa. "BRIDGING AUTOMATIC PROBLEM GENERATION SYSTEM AND LEARNING SUPPORT SYSTEM FOR HUMAN'S PROBLEM GENERATION." INTELLIGENT MEDIA INTEGRATION NAGOYA UNIVERSITY / COE, 2006. http://hdl.handle.net/2237/10477.

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Yusuf, Syed Adnan. "An evolutionary AI-based decision support system for urban regeneration planning." Thesis, University of Wolverhampton, 2010. http://hdl.handle.net/2436/114896.

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The renewal of derelict inner-city urban districts suffering from high levels of socio-economic deprivation and sustainability problems is one of the key research areas in urban planning and regeneration. Subject to a wide range of social, economical and environmental factors, decision support for an optimal allocation of residential and service lots within such districts is regarded as a complex task. Pre-assessment of various neighbourhood factors before the commencement of actual location allocation of various public services is considered paramount to the sutainable outcome of regeneration projects. Spatial assessment in such derelict built-up areas requires planning of lot assignment for residential buildings in a way to maximize accessibility to public services while minimizing the deprivation of built neighbourhood areas. However, the prediction of socio-economic deprivation impact on the regeneration districts in order to optimize the location-allocation of public service infrastructure is a complex task. This is generally due to the highly conflicting nature of various service structures with various socio-economic and environmental factors. In regards to the problem given above, this thesis presents the development of an evolutionary AI-based decision support systemto assist planners with the assessment and optimization of regeneration districts. The work develops an Adaptive Network Based Fuzzy Inference System (ANFIS) based module to assess neighbourhood districts for various deprivation factors. Additionally an evolutionary genetic algorithms based solution is implemented to optimize various urban regeneration layouts based upon the prior deprivation assessment model. The two-tiered framework initially assesses socio-cultural deprivation levels of employment, health, crime and transport accessibility in neighbourhood areas and produces a deprivation impact matrix overthe regeneration layout lots based upon a trained, network-based fuzzy inference system. Based upon this impact matrix a genetic algorithm is developed to optimize the placement of various public services (shopping malls, primary schools, GPs and post offices) in a way that maximize the accessibility of all services to regenerated residential units as well as contribute to minimize the measure of deprivation of surrounding neighbourhood areas. The outcome of this research is evaluated over two real-world case studies presenting highly coherent results. The work ultimately produces a smart urban regeneration toolkit which provides designer and planner decision support in the form of a simulation toolkit.
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Chapman, Dona Elizabeth. "A decision support system for the faculty/course assignment problem." Thesis, This resource online, 1985. http://scholar.lib.vt.edu/theses/available/etd-10022008-063148/.

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Bergey, Paul K. "A Decision Support System for the Electrical Power Districting Problem." Diss., Virginia Tech, 2000. http://hdl.handle.net/10919/27347.

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Due to a variety of political, economic, and technological factors, many national electricity industries around the globe are transforming from non-competitive monopolies with centralized systems to decentralized operations with competitive business units. This process, commonly referred to as deregulation (or liberalization) is driven by the belief that a monopolistic industry fails to achieve economic efficiency for consumers over the long run. Deregulation has occurred in a number of industries such as: aviation, natural gas, transportation, and telecommunications. The most recent movement involving the deregulation of the electricity marketplace is expected to yield consumer benefit as well.

To facilitate deregulation of the electricity marketplace, competitive business units must be established to manage various functions and services independently. In addition, these business units must be given physical property rights for certain parts of the transmission and distribution network in order to provide reliable service and make effective business decisions. However, partitioning a physical power grid into economically viable districts involves many considerations. We refer to this complex problem as the electrical power districting problem.

This research is intended to identify the necessary and fundamental characteristics to appropriately model and solve an electrical power districting problem. Specifically, the objectives of this research are five-fold. First, to identify the issues relevant to electrical power districting problems. Second, to investigate the similarities and differences of electrical power districting problems with other districting problems published in the research literature. Third, to develop and recommend an appropriate solution methodology for electrical power districting problems. Fourth, to demonstrate the effectiveness of the proposed solution method for a specific case of electric power districting in the Republic of Ghana, with data provided by the World Bank. Finally, to develop a decision support system for the decision makers at the World Bank for solving Ghana's electrical power districting problem.
Ph. D.

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Zhang, Yi. "Synthesis of optimum HVAC system configurations by evolutionary algorithm." Thesis, Loughborough University, 2005. https://dspace.lboro.ac.uk/2134/7714.

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The HVAC system configuration is a conceptual design of the HVAC system, including the employed components, the topology of the airflow network, and the control strategy with set points. Selection of HVAC system configuration is normally done in the early stage of the design process. The configuration design, however, has significant impacts on the performance of the final system. This thesis describes the development of the design synthesis of optimal HVAC system configurations by Evolutionary Algorithm. In this research, the HVAC system configuration design synthesis has been formulated as an optimisation problem, in which, the component set of the configuration, the topology of the airflow network, and the control set points for the assumed supervisory control strategy, are the optimisation variables. Psychrometrics-based configuration model has been developed in order to evaluate the optimisation objective of minimising the annual energy consumption of the HVAC system. The optimisation is also subjected to a number of design constraints, including the connectivity of the topology, the performance limitations of the components, and the design requirements for the air-conditioned zones. The configuration synthesis problem is a multi-level optimisation problem. The topology depends on the set of selected components, whereas the search space of the control set points changes with the different components and topology. On the other hand, the performance of the configuration is assessed with its optimum operation; therefore the control set points have to be optimised for each configuration solution, before the optimum configuration can be identified. In this research, a simultaneous evolutionary approach has been developed. All optimisation variables of the configuration have been enwded into an integrated genotypic data structure. Evolutionary operators have also been developed to search the topological space (for the optimum topology) and parametric space (for the optimal control set points) at the same time. The performance of the developed approach has been validated with example optimisation problems. It is concluded that the implemented evolutionary algorithm has been able to find (near) optimum solutions for various design problems, though multiple trials may be required. The limitations of this approach and the direction of future development have been discussed.
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Brandao, Jose Carlos Soares. "A decision support system and algorithms for the vehicle routing and scheduling problem." Thesis, Lancaster University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.238909.

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Bird, Simon N. "Decision problem structuring for selection of fixed firefighting systems." Thesis, Loughborough University, 2014. https://dspace.lboro.ac.uk/2134/17409.

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Active fire protection systems are an essential fire safety management tool, particularly in potentially high financial and risk consequence scenarios. In the UK and Europe over recent decades regulatory changes have been successful in creating an environment in which more innovation can take place. Increased numbers of fixed firefighting system types are now available to the user. However, not all systems offered are equal in terms of; suitability, cost, maturity of supporting knowledge, and overall performance or in-service reliability. Understanding of the systems performance and its limitations and how to match this to the assessed fire risk is incomplete among users. Experts are observing increasing numbers of what they consider to be poor fixed firefighting system choices leading to weaker fire safety designs, which is a cause of concern. Therefore the research aim is to verify that these concerns are founded and, that being the case, to develop a decision support system and related supporting resources to further this aspect of fire safety education and enable users to make better informed system selections. Thus, the focus of this research has been to develop a fixed firefighting system selection tool to complement existing legislation, which incorporates logic, rules and fire safety educational resources in a variety of formats to aid the fire safety design process. A variety of largely heuristic techniques have been used to aggregate data to form knowledge to underpin fixed firefighting system selection tool. In this form, the tool has been validated by experts as being a useful resource. The developed tool also provides ample opportunity for useful ongoing future development. The work recognises that cost and benefit are critical in the selection process. Supporting resources have been incorporated into the tool to assist users in evaluating the levels of reliability they might expect from a system in their circumstances. This tool has now been exposed to a wider audience of experts as part of an evaluation process. Findings include: that the tool is an innovative approach to promoting good fire safety designs, the tool efficiently provides useful fire safety education to users and the developed supporting resources which consider firefighting system reliability are helpful. This thesis and reference papers summarise the key stages of this research and tool development. The thesis concludes by outlining the progress achieved by this work and recommendations arising.
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Hawken, Leanne S. "Evaluation of a targeted group intervention within a school-wide system of behavior support /." view abstract or download file of text, 2002. http://wwwlib.umi.com/cr/uoregon/fullcit?p3055691//.

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Thesis (Ph. D.)--University of Oregon, 2002.
Typescript. Includes vita and abstract. Includes bibliographical references (leaves 99-105). Also available for download via the World Wide Web; free to University of Oregon users.
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Books on the topic "Evolutionary problem support system"

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Weitz, Rob R. Solving a multi-criteria allocation problem: A decision support system approach. Fontainebleau: INSEAD, 1991.

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Dickey, John W. Cyberquest: Problem solving and innovation support system conceptual background and experiences. Norwood, N.J: Ablex Pub., 1995.

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Liao, Shu-hsien. Case-based decision support system: A problem solving methodology for military command and control. [s.l.]: typescript, 1996.

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Blackwell, C. C. A study of the control problem of the shoot side environment delivery system of a closed crop growth research chamber. Moffett FIeld, Calif: National Aeronautics and Space Administration, Ames Research Center, 1992.

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Hoshika, Akira. Kaiyō gomi taisaku no kakuritsu ni muketa jōhō shien shisutemu no kōchiku ni kansuru kenkyū: Study on the establishment of an information support system for the marine litter problem. Ibaraki-ken Tsukuba-shi: Sangyō Gijutsu Sōgō Kenkyūjo Chishitsu Chōsa Sōgō Sentā, 2012.

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Review the national security ramifications of the year 2000 computer problem: Hearing before the Subcommittee on Readiness and Management Support of the Committee on Armed Services, United States Senate, One Hundred Sixth Congress, first session, February 24, 1999. Washington: U.S. G.P.O., 2000.

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Vlasyenko, Nikolay, Artem Tsirin, YEkatyerina Spyektor, Natalya Povetkina, Zarina Bedoeva, Yuliya Belyaeva, Maksim Zaloilo, Elena Rafalyuk, and E. Sidorova. Dictionary on the Subject of Anti-Corruption. ru: INFRA-M Academic Publishing LLC., 2016. http://dx.doi.org/10.12737/18663.

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Currently, the problem of combating corruption is in the center of attention of Russian society and the state. The legal and organizational framework for combating corruption has been formed. Anti-corruption legislation is constantly being improved, becoming more holistic and systematic, so further classification of its concepts is required. The Glossary contains more than 500 terms of Russian and foreign language origin, which are basic in the practice of combating corruption and are used in criminal, administrative and financial law of Russia; it guides the reader in a complex system of modern legal categories related to anti-corruption topics; uses the tools of international agreements ratified by the Russian Federation; it will help clarify the conceptual apparatus of normative legal acts and eliminate contradictions in existing documents. The publication is intended to be used in the educational process in the framework of scientific and educational support for combating corruption. For employees of scientific institutions and government agencies, teachers, students, postgraduates of higher educational institutions and practicing lawyers.
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United States. National Aeronautics and Space Administration., ed. Communications system evolutionary scenarios for Martian SEI support: Final report. Reston, VA: Stanford Telecom, 1992.

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Communications system evolutionary scenarios for Martian SEI support: Final report. Reston, VA: Stanford Telecom, 1992.

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Dickey, John W. CyberQuest: Problem Solving and Innovation Support System, Conceptual Background and Experiences (Publications in Creativity Research). Ablex Publishing, 1993.

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Book chapters on the topic "Evolutionary problem support system"

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Liu, Wenchang. "Discussion on Enterprise Emergency Management Decision Support System." In Intelligence Computation and Evolutionary Computation, 73–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31656-2_11.

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Bui, Thang N., and Gnanasekaran Sundarraj. "Ant System for the k-Cardinality Tree Problem." In Genetic and Evolutionary Computation – GECCO 2004, 36–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24854-5_4.

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Dreżewski, Rafał, Piotr Woźniak, and Leszek Siwik. "Agent-Based Evolutionary System for Traveling Salesman Problem." In Lecture Notes in Computer Science, 34–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02319-4_5.

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Gajda, Ewa, Robert Schaefer, and Maciej Smołka. "Evolutionary Multiobjective Optimization Algorithm as a Markov System." In Parallel Problem Solving from Nature, PPSN XI, 617–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15844-5_62.

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de Almeida, Carolina P., Richard A. Gonçalves, and Myriam R. Delgado. "A Hybrid Immune-Based System for the Protein Folding Problem." In Evolutionary Computation in Combinatorial Optimization, 13–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-71615-0_2.

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Xianghui, Dong, and Dai Ruwei. "Study Diploid System by a Hamiltonian Cycle Problem Algorithm." In Genetic and Evolutionary Computation — GECCO 2003, 152–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45105-6_19.

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Cvetkovic, Dragan, and Ian Parmee. "Agent-based Support within an Interactive Evolutionary Design System." In Adaptive Computing in Design and Manufacture V, 355–67. London: Springer London, 2002. http://dx.doi.org/10.1007/978-0-85729-345-9_30.

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Saruwatari, Junichi, and Masafumi Hagiwara. "Ikebana Support System Reflecting Kansei with Interactive Evolutionary Computation." In Entertainment Computing - ICEC 2005, 456–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11558651_44.

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Garrett, Deon, Dipankar Dasgupta, Joseph Vannucci, and James Simien. "Applying Hybrid Multiobjective Evolutionary Algorithms to the Sailor Assignment Problem." In Advances in Evolutionary Computing for System Design, 269–301. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72377-6_12.

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Srinivasan, Dipti, and Min Chee Choy. "Distributed Problem Solving using Evolutionary Learning in Multi-Agent Systems." In Advances in Evolutionary Computing for System Design, 211–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72377-6_9.

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Conference papers on the topic "Evolutionary problem support system"

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Chen, Yao, jia-zhen Huo, and Hu Li. "Optimal model, algorism and decision support system of bulk ship loading problem." In 2008 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2008. http://dx.doi.org/10.1109/cec.2008.4631039.

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Saidi, Majdi, Zhongliang Li, Rachid Outbib, Seifeddine Benelghali, Thiery Le roux, and Emmanuel Cardone. ""Realising optimum design of a hybrid renewable energy system using multiobjective evolutionary algorithm"." In The 11th International Conference on Integrated Modeling and Analysis in Applied Control and Automation. CAL-TEK srl, 2018. http://dx.doi.org/10.46354/i3m.2018.imaaca.010.

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"This paper proposes a strategy to find the combined design of hybrid PV/wind electric system and the policy of financial support of state. Different from the most existing proposals, this study formalizes the problem as a multi-objective optimization considering the benefits of both user and supplier. Multi-objective evolution algorithm based on decomposition (MOEA/D) is adopted to solve the formulated problem. The proposed strategy is applied in the case of a company located in the southeast of France. The results validate the effectiveness of the proposal."
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Pinto, Tiago, and Zita Vale. "AiD-EM: Adaptive Decision Support for Electricity Markets Negotiations." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/957.

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This paper presents the Adaptive Decision Support for Electricity Markets Negotiations (AiD-EM) system. AiD-EM is a multi-agent system that provides decision support to market players by incorporating multiple sub-(agent-based) systems, directed to the decision support of specific problems. These sub-systems make use of different artificial intelligence methodologies, such as machine learning and evolutionary computing, to enable players adaptation in the planning phase and in actual negotiations in auction-based markets and bilateral negotiations. AiD-EM demonstration is enabled by its connection to MASCEM (Multi-Agent Simulator of Competitive Electricity Markets).
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Abu-Mahfouz, Issam, and Amit Banerjee. "Bearing Fault Parameter Identification Under Varying Operating Conditions Using Vibration Signals and Evolutionary Algorithms." In ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-39124.

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This paper presents an effective bearing fault parameter identification scheme based on evolutionary optimization techniques. Three seeded faults in the rotating machinery supported by the test roller bearing include inner race fault, outer race fault and a single ball defect. The fault related features are extracted experimentally by processing the acquired vibration signals in both the time and frequency domain. Techniques based on the power spectral density (PSD) and wavelet transform (WT) are utilized for feature extraction. The sensitivity of the proposed method is investigated under varying operating speeds and radial bearing load. In this study, the inverse problem of parameter identification is investigated. The problem of parameter identification is recast as an optimization problem and two well known evolutionary algorithms, differential evolution (DE) and particle swarm optimization (PSO), are used to identify system parameters given a system response. For online parameter identification, differential evolution outperforms particle both in terms of adaptability and tighter convergence properties. The distinction between the two methods is not distinctively obvious on the offline parameter identification problem.
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Pratt, David M., and David J. Moorhouse. "Common Currency for System Integration of High Intensity Energy Subsystems." In ASME/JSME 2011 8th Thermal Engineering Joint Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/ajtec2011-44013.

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Aerospace vehicle design has progressed in an evolutionary manner, with certain discrete changes such as turbine engines replacing propellers for higher speeds. The evolution has worked very well for commercial aircraft because the major components can be optimized independently. This is not true for many military configurations which require a more integrated approach. In addition, the introduction of aspects for which there is no pre-existing database requires special attention. Examples of subsystem that have no pre-existing data base include directed energy weapons (DEW) such as high power microwaves (HPM) and high energy lasers (HEL). These devices are inefficient, therefore a large portion of the energy required to operate the device is converted to waste heat and must be transferred to a suitable heat sink. For HPM, the average heat load during one ‘shot’ is on the same order as traditional subsystems and thus designing a thermal management system is possible. The challenge is transferring the heat from the HPM device to a heat sink. The power density of each shot could be hundreds of megawatts. This heat must be transferred from the HPM beam dump to a sink. The heat transfer must occur at a rate that will support shots in the 10–100Hz range. For HEL systems, in addition to the high intensity, there are substantial system level thermal loads required to provide an ‘infinite magazine.’ Present models are inadequate to analyze these problems, current systems are unable to sustain the energy dissipation required and the high intensity heat fluxes applied over a very short duration phenomenon is not well understood. These are examples of potential future vehicle integration challenges. This paper addresses these and other subsystems integration challenges using a common currency for vehicle optimization. Exergy, entropy generation minimization, and energy optimization are examples of methodologies that can enable the creation of energy optimized systems. These approaches allow the manipulation of fundamental equations governing thermodynamics, heat transfer, and fluid mechanics to produce minimized irreversibilities at the vehicle, subsystem and device levels using a common currency. Applying these techniques to design for aircraft system-level energy efficiency would identify not only which subsystems are inefficient but also those that are close to their maximum theoretical efficiency while addressing diverse system interaction and optimal subsystem integration. Such analyses would obviously guide researchers and designers to the areas having the highest payoff and enable departures from the evolutionary process and create a breakthrough design.
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Sciubba, Enrico. "Extended Exergy as a Locally Significant Environmental Indicator." In ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-63649.

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The concept of “environmental impact” has evolved to encompass a multi-disciplinary and quite detailed examination of the local and global implications of the interactions of anthropic processes with the biosphere. Sets of quantitative measures of the interaction, called Environmental Indicators (EI), have been proposed with the intent of providing a synthetic, accurate and reliable decision support basis for planners and decision makers. This approach is not devoid of drawbacks: generality conflicts with specificity and it is often difficult to connect a local EI with a more global measure of environmental impact. Furthermore, several of the proposed EIs lack a sound physical basis (in particular, they are not always rigorously rooted on thermodynamics). This paper proposes a method to bridge both gaps by introducing an EI derived strictly from prime thermodynamic concepts (the extended exergy cost, EEC or cee) and by defining a procedure to apply it to local and global scales alike. It is argued that such an EI successfully solves the “externalities” problem for industrial energy conversion systems and that it can also be employed to assess the evolutionary patterns of natural systems. All applications to date demonstrate that cee is indeed a useful tool for the quantification of the primary equivalent resource costs and for their proper internalization in energy systems analyses.
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Leyva Lopez, Juan Carlos, Jesus Jaime Solano Noriega, Diego Alonso Gastelum Chavira, and Maria De los Dolores Sanchez Castaneda. "A Multiobjective Evolutionary Approach to a Medium- Sized Multicriteria Ranking Problem A Multiobjective Evolutionary Approach to a Medium- Sized Multicriteria Ranking Problem." In Fourth International Workshop on Knowledge Discovery, Knowledge Management and Decision Support. Paris, France: Atlantis Press, 2013. http://dx.doi.org/10.2991/.2013.23.

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Дергачева, Елена, and Elena Dergacheva. "Visualizing Socio-techno-natural Processes: Issues and Challenges." In 29th International Conference on Computer Graphics, Image Processing and Computer Vision, Visualization Systems and the Virtual Environment GraphiCon'2019. Bryansk State Technical University, 2019. http://dx.doi.org/10.30987/graphicon-2019-2-168-172.

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Contemporary models and systems of data visualization, implemented on the basis of information technology, individually cover social, technological and natural processes of the world development. Modern world is developing in the conditions of transformational transitional processes, when the artificial shell, the technosphere, created by the society becomes the leading life-support system instead of the biosphere. It is a full-fledged participant in the exchange processes between a globalizing technogenic society and transforming nature, which allows us to talk about forming socio-techno-natural laws of developing the world and life. There are no integrative visual models in the world that represent evolutionary changes in three systems simultaneously – society (and man), the technosphere and the technologically transformed biosphere, on the basis of which it is possible to predict the formation of a sustainable future for humanity in connection with the expansion of socio-technonatural processes. The generally recognized visualization methodology must be supplemented by the methodology of the philosophy of the world socio-technogenic development and the change in the evolution of life for a better visual representation and explanation of the transformation processes taking place in the changing world that is becoming post-biospheric. Interdisciplinary philosophical view allows capturing the world in the integrity of its diverse characteristics and at the same time forming a problem field for visualizing transformational processes. The fundamental role of the integration link belongs to information technology, which allows us to represent visually heterogeneous data with the aim of further developing models for the safe development of mankind in the technosphere.
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Urquhart, Neil, and Achille Fonzone. "Evolving solution choice and decision support for a real-world optimisation problem." In GECCO '17: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3071178.3071207.

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Sato, Wataru, and Naoyuki Kubota. "Interactive evolutionary computation for robot design support system." In 2009 IEEE International Symposium on Computational Intelligence in Robotics and Automation - (CIRA 2009). IEEE, 2009. http://dx.doi.org/10.1109/cira.2009.5423185.

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Reports on the topic "Evolutionary problem support system"

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Sturzenegger, Germán, Cecilia Vidal, and Sebastián Martínez. The Last Mile Challenge of Sewage Services in Latin America and the Caribbean. Edited by Anastasiya Yarygina. Inter-American Development Bank, November 2020. http://dx.doi.org/10.18235/0002878.

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Access to piped sewage in Latin America and the Caribbean (LAC) cities has been on the rise in recent decades. Yet achieving high rates of end-user connection between dwellings and sewage pipelines remains a challenge for water and sanitation utilities. Governments throughout the region are investing millions in increasing access to sewage services but are failing in the last mile. When households do not connect to the sewage system, the full health and social benefits of sanitation investments fail to accrue, and utilities can face lost revenue and higher operating costs. Barriers to connect are diverse, including low willingness to pay for connection costs and/or the associated tariffs, liquidity and credit constrains to cover the cost of upgrades or repairs, information gaps on the benefits of connecting, behavioral obstacles, and collective action failures. In contexts of weak regulation and strong social pressure, utilities typically lack the ability to enforce connection through fines and legal action. This paper explores the scope of the connectivity problem, identifies potential connection barriers, and discusses policy solutions. A research agenda is proposed in support of evidence-based interventions that have the potential to achieve higher effective sanitation coverage more rapidly and cost-effectively in LAC. This research agenda must focus on: i) quantifying the scope of the problem; ii) understanding the barriers that trigger it; and iii) identifying the most cost-effective policy and market-based solutions.
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McKay, Tasseli, Megan Comfort, Justin Landwehr, Erin Kennedy, and Oliver Williams. Partner Violence Help-Seeking in Couples Affected by Incarceration: Overcoming Barriers. RTI Press, March 2020. http://dx.doi.org/10.3768/rtipress.2020.pb.0021.2004.

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Efforts to support help-seeking by victims of partner violence in couples affected by incarceration represent a key part of larger efforts in the fields of domestic violence and victim services to improve the accessibility of services in marginalized communities and better meet complex victim needs. Qualitative data from 167 Multi-site Family Study participants suggest that involvement with the criminal justice system (whether directly or through a family member) introduces unique individual, interpersonal, and sociocultural barriers to defining one’s experiences as a problem, deciding to seek help, and selecting sources of help. Opportunities exist not only to tailor service delivery approaches in ways that overcome the individual and interpersonal obstacles that affect victims but also to pursue longer-range shifts in public policy and community infrastructure that will address broader and more-entrenched barriers to help-seeking.
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Perdigão, Rui A. P., and Julia Hall. Spatiotemporal Causality and Predictability Beyond Recurrence Collapse in Complex Coevolutionary Systems. Meteoceanics, November 2020. http://dx.doi.org/10.46337/201111.

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Causality and Predictability of Complex Systems pose fundamental challenges even under well-defined structural stochastic-dynamic conditions where the laws of motion and system symmetries are known. However, the edifice of complexity can be profoundly transformed by structural-functional coevolution and non-recurrent elusive mechanisms changing the very same invariants of motion that had been taken for granted. This leads to recurrence collapse and memory loss, precluding the ability of traditional stochastic-dynamic and information-theoretic metrics to provide reliable information about the non-recurrent emergence of fundamental new properties absent from the a priori kinematic geometric and statistical features. Unveiling causal mechanisms and eliciting system dynamic predictability under such challenging conditions is not only a fundamental problem in mathematical and statistical physics, but also one of critical importance to dynamic modelling, risk assessment and decision support e.g. regarding non-recurrent critical transitions and extreme events. In order to address these challenges, generalized metrics in non-ergodic information physics are hereby introduced for unveiling elusive dynamics, causality and predictability of complex dynamical systems undergoing far-from-equilibrium structural-functional coevolution. With these methodological developments at hand, hidden dynamic information is hereby brought out and explicitly quantified even beyond post-critical regime collapse, long after statistical information is lost. The added causal insights and operational predictive value are further highlighted by evaluating the new information metrics among statistically independent variables, where traditional techniques therefore find no information links. Notwithstanding the factorability of the distributions associated to the aforementioned independent variables, synergistic and redundant information are found to emerge from microphysical, event-scale codependencies in far-from-equilibrium nonlinear statistical mechanics. The findings are illustrated to shed light onto fundamental causal mechanisms and unveil elusive dynamic predictability of non-recurrent critical transitions and extreme events across multiscale hydro-climatic problems.
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