Articles de revues : « Genetic programming, strategies, applications »

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Ni, He, Fan Ming Zeng, Bo Yu et Feng Rui Sun. « The Convergence Mechanism and Strategies for Non-Elitist Genetic Programming ». Applied Mechanics and Materials 347-350 (août 2013) : 3850–60. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3850.

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Genetic programming is an evolutionary algorithm that proposed to solve the automatic computer program design problem by J.R.Koza in the 1990s. It has good universality and intelligence, and has been widely applied in the field of computer engineering. But genetic programming is essentially a stochastic optimization algorithm, lack theoretic basis on the convergence of algorithm, which limit the scope of its application in some extent. The convergence mechanism of non-elitist genetic programming was studied in this paper. A recursive estimation of the probability of population contains satisfactory solution with the evolution algebra was established by the analysis of operators characteristic parameters, then a sufficient condition of population converge in probability was derived from this estimation, and thereby some operational convergence strategies for many common evolution modes were provided.

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Zhang, Biaobiao, Yue Wu, Jiabin Lu et K. L. Du. « Evolutionary Computation and Its Applications in Neural and Fuzzy Systems ». Applied Computational Intelligence and Soft Computing 2011 (2011) : 1–20. http://dx.doi.org/10.1155/2011/938240.

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Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general-purpose stochastic global optimization approach under the universally accepted neo-Darwinian paradigm, which is a combination of the classical Darwinian evolutionary theory, the selectionism of Weismann, and the genetics of Mendel. Evolutionary algorithms are a major approach to adaptation and optimization. In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. Some topics pertaining to evolutionary algorithms are also discussed, and a comparison between evolutionary algorithms and simulated annealing is made. Finally, the application of EAs to the learning of neural networks as well as to the structural and parametric adaptations of fuzzy systems is also detailed.

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Li, He, et Naiyu Shi. « Application of Genetic Optimization Algorithm in Financial Portfolio Problem ». Computational Intelligence and Neuroscience 2022 (15 juillet 2022) : 1–9. http://dx.doi.org/10.1155/2022/5246309.

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In order to address the application of genetic optimization algorithms to financial investment portfolio issues, the optimal allocation rate must be high and the risk is low. This paper uses quadratic programming algorithms and genetic algorithms as well as quadratic programming algorithms, Matlab planning solutions for genetic algorithms, and genetic algorithm toolboxes to solve Markowitz’s mean variance model. The mathematical model for introducing sparse portfolio strategies uses the decomposition method of penalty functions as an algorithm for solving nonconvex sparse optimization strategies to solve financial portfolio problems. The merging speed of the quadratic programming algorithm is fast, and the merging speed depends on the selection of the initial value. The genetic algorithm performs very well in global searches, but local search capabilities are insufficient and the pace of integration into the next stage is slow. To solve this, using a genetic algorithm toolbox is quick and easy. The results of the experiments show that the final solution of the decomposition method of the fine function is consistent with the solution of the integrity of the genetic algorithm. 67% of the total funds will be spent on local car reserves and 33% on wine reserves. When data scales are small, quadratic programming algorithms and genetic algorithms can provide effective portfolio feedback, and the method of breaking down penalty functions to ensure the reliability and effectiveness of algorithm combinations is widely used in sparse financial portfolio issues.

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Sokolov, Artem, Darrell Whitley et Andre’ da Motta Salles Barreto. « A note on the variance of rank-based selection strategies for genetic algorithms and genetic programming ». Genetic Programming and Evolvable Machines 8, n^o 3 (31 juillet 2007) : 221–37. http://dx.doi.org/10.1007/s10710-007-9030-1.

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KABOUDAN, MAK. « EXTENDED DAILY EXCHANGE RATES FORECASTS USING WAVELET TEMPORAL RESOLUTIONS ». New Mathematics and Natural Computation 01, n^o 01 (mars 2005) : 79–107. http://dx.doi.org/10.1142/s1793005705000056.

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Applying genetic programming and artificial neural networks to raw as well as wavelet-transformed exchange rate data showed that genetic programming may have good extended forecasting abilities. Although it is well known that most predictions of exchange rates using many alternative techniques could not deliver better forecasts than the random walk model, in this paper employing natural computational strategies to forecast three different exchange rates produced two extended forecasts (that go beyond one-step-ahead) that are better than naïve random walk predictions. Sixteen-step-ahead forecasts obtained using genetic programming outperformed the one- and sixteen-step-ahead random walk US dollar/Taiwan dollar exchange rate predictions. Further, sixteen-step-ahead forecasts of the wavelet-transformed US dollar/Japanese Yen exchange rate also using genetic programming outperformed the sixteen-step-ahead random walk predictions of the exchange rate. However, random walk predictions of the US dollar/British pound exchange rate outperformed all forecasts obtained using genetic programming. Random walk predictions of the same three exchange rates employing raw and wavelet-transformed data also outperformed all forecasts obtained using artificial neural networks.

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Niño, Elías, Carlos Ardila, Alfredo Perez et Yezid Donoso. « A Genetic Algorithm for Multiobjective Hard Scheduling Optimization ». International Journal of Computers Communications & ; Control 5, n^o 5 (1 décembre 2010) : 825. http://dx.doi.org/10.15837/ijccc.2010.5.2243.

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This paper proposes a genetic algorithm for multiobjective scheduling optimization based in the object oriented design with constrains on delivery times, process precedence and resource availability. Initially, the programming algorithm (PA) was designed and implemented, taking into account all constraints mentioned. This algorithm’s main objective is, given a sequence of production orders, products and processes, calculate its total programming cost and time. Once the programming algorithm was defined, the genetic algorithm (GA) was developed for minimizing two objectives: delivery times and total programming cost. The stages defined for this algorithm were: selection, crossover and mutation. During the first stage, the individuals composing the next generation are selected using a strong dominance test. Given the strong restrictions on the model, the crossover stage utilizes a process level structure (PLS) where processes are grouped by its levels in the product tree. Finally during the mutation stage, the solutions are modified in two different ways (selected in a random fashion): changing the selection of the resources of one process and organizing the processes by its execution time by level. In order to obtain more variability in the found solutions, the production orders and the products are organized with activity planning rules such as EDD, SPT and LPT. For each level of processes, the processes are organized by its processing time from lower to higher (PLU), from higher to lower (PUL), randomly (PR), and by local search (LS). As strategies for local search, three algorithms were implemented: Tabu Search (TS), Simulated Annealing (SA) and Exchange Deterministic Algorithm (EDA). The purpose of the local search is to organize the processes in such a way that minimizes the total execution time of the level. Finally, Pareto fronts are used to show the obtained results of applying each of the specified strategies. Results are analyzed and compared.

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Chernov, Ivan E., et Andrey V. Kurov. « APPLICATION OF GENETIC ALGORITHMS IN CRYPTOGRAPHY ». RSUH/RGGU Bulletin. Series Information Science. Information Security. Mathematics, n^o 1 (2022) : 63–82. http://dx.doi.org/10.28995/2686-679x-2022-1-63-82.

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Currently in the development of computer technologies that ensure information security and information protection, cryptographic methods of protection are widely used. The main tasks in cryptography are the development of new encryption features, difficult to break and repetitive ciphers. To solve that problem, falling into the class of NP-complete ones, algorithms based on natural principles have been used in recent years. These include genetic algorithms (GA), evolutionary methods, swarm intelligence algorithms. In models and algorithms of evolutionary computations, the construction of basic models and rules is implemented, according to which it can change (evolve). In recent years, evolutionary computing schemes have been proposed, including the genetic algorithm, genetic programming, evolutionary programming, and evolutionary strategies. The paper discusses the existing cryptography methods, basic concepts and methods of modern cryptography, the notion of a genetic algorithm, a universal hash function, as well as a hash detection method and a genetic hashing algorithm built on it. A genetic algorithm was implemented in the Golang language, modified for the current problem of finding the optimal hash functions. A detailed description of each stage of the algorithm execution is given. Also, within the framework of the research, a study of the function of the genetic algorithm itself and the genetic hashing algorithm was carried out, evaluating the convergence of the genetic algorithm depending on the input data, and evaluating the possible direction of further research.

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FREY, CLEMENS. « CO-EVOLUTION OF FINITE STATE MACHINES FOR OPTIMIZATION : PROMOTION OF DEVICES WHICH SEARCH GLOBALLY ». International Journal of Computational Intelligence and Applications 02, n^o 01 (mars 2002) : 1–16. http://dx.doi.org/10.1142/s1469026802000397.

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In this work a co-evolutionary approach is used in conjunction with Genetic Programming operators in order to find certain transition rules for two-step discrete dynamical systems. This issue is similar to the well-known artificial-ant problem. We seek the dynamic system to produce a trajectory leading from given initial values to a maximum of a given spatial functional.This problem is recast into the framework of input-output relations for controllers, and the optimization is performed on program trees describing input filters and finite state machines incorporated by these controllers simultaneously. In the context of Genetic Programming there is always a set of test cases which has to be maintained for the evaluation of program trees. These test cases are subject to evolution here, too, so we employ a so-called host-parasitoid model in order to evolve optimizing dynamical systems.Reinterpreting these systems as algorithms for finding the maximum of a functional under constraints, we have derived a paradigm for the automatic generation of adapted optimization algorithms via optimal control. We provide numerical examples generated by the GP-system MathEvEco. These examples refer to key properties of the resulting strategies and they include statistical evidence showing that for this problem of system identification the co-evolutionary approach is superior to standard Genetic Programming.

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Edmondson, Michael C., L. Tang et A. Kern. « Software Development Modules for Microcontroller-Based System ». Advances in Science and Technology 56 (septembre 2008) : 45–51. http://dx.doi.org/10.4028/www.scientific.net/ast.56.45.

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Microcontrollers are small devices commonly used for control purposes over a wide range of applications. As the control strategies and the selection of hardware differ from one application to another, it is a common practice for engineers to develop the application programs based-on the selected hardware and the control methodologies. Such development process requires time for programming and testing, especially for large projects which need to interface and integrate with a number of different hardware and software. This paper presents an approach using the concept of soft development modules to shorten the application program development time for control systems using microcontrollers. A set of soft modules has been developed for a widely used microcontroller. The testing conducted on the serial communication and fuzzy logic control modules successfully produced application programs within a much shorter time, and reduced human programming errors. The application of soft development modules will allow system developers to focus on the system design level without the need to spend large amount of time on generic programming details. Such an approach provides a useful programming development platform for future applications using microcontrollers.

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GAGNÉ, CHRISTIAN, et MARC PARIZEAU. « GENERICITY IN EVOLUTIONARY COMPUTATION SOFTWARE TOOLS : PRINCIPLES AND CASE-STUDY ». International Journal on Artificial Intelligence Tools 15, n^o 02 (avril 2006) : 173–94. http://dx.doi.org/10.1142/s021821300600262x.

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This paper deals with the need for generic software development tools in evolutionary computations (EC). These tools will be essential for the next generation of evolutionary algorithms where application designers and researchers will need to mix different combinations of traditional EC (e.g. genetic algorithms, genetic programming, evolutionary strategies, etc.), or to create new variations of these EC, in order to solve complex real world problems. Six basic principles are proposed to guide the development of such tools. These principles are then used to evaluate six freely available, widely used EC software tools. Finally, the design of Open BEAGLE, the framework developed by the authors, is presented in more detail.

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Xu, Liang. « Optimal Tolling Strategies and Concession Period Required for Build-Operate-Transfer Road Projects in Network with Demand Uncertainty by Vehicle Types ». Applied Mechanics and Materials 209-211 (octobre 2012) : 977–86. http://dx.doi.org/10.4028/www.scientific.net/amm.209-211.977.

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In view of uncertainty of traffic demand by vehicle types during the concession period, the toll charges of these BOT roads should be time-dependent and varied for different vehicle types by time of the concession period so as to maximize the system performance.It can be formulated as a bi-level programming problem. At the upper level, the objective is to maximize the social surplus combining of consumer surplus and the investor’s net profit. With taking account the demand elasticity in respect to the toll variability and incorporating the demand uncertainty into the revenue-cost constraint, the lower level is a multi-class reliability-based stochastic user equilibrium model. A genetic solution algorithm is adopted for solving the bi-level programming problem. A numerical example is presented to illustrate the applications of the proposed model and solution algorithm and some conclusions are drawn.

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Xu, Qiang, Qiuwen Chen et Weifeng Li. « Application of genetic programming to modeling pipe failures in water distribution systems ». Journal of Hydroinformatics 13, n^o 3 (28 octobre 2010) : 419–28. http://dx.doi.org/10.2166/hydro.2010.189.

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The water loss from a water distribution system is a serious problem for many cities, which incurs enormous economic and social loss. However, the economic and human resource costs to exactly locate the leakage are extraordinarily high. Thus, reliable and robust pipe failure models are demanded to assess a pipe's propensity to fail. Beijing City was selected as the case study area and the pipe failure data for 19 years (1987–2005) were analyzed. Three different kinds of methods were applied to build pipe failure models. First, a statistical model was built, which discovered that the ages of leakage pipes followed the Weibull distribution. Then, two other models were developed using genetic programming (GP) with different data pre-processing strategies. The three models were compared thereafter and the best model was applied to assess the criticality of all the pipe segments of the entire water supply network in Beijing City based on GIS data.

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Olague, Gustavo, Jose Armando Menendez-Clavijo, Matthieu Olague, Arturo Ocampo, Gerardo Ibarra-Vazquez, Rocio Ochoa et Roberto Pineda. « Automated Design of Salient Object Detection Algorithms with Brain Programming ». Applied Sciences 12, n^o 20 (21 octobre 2022) : 10686. http://dx.doi.org/10.3390/app122010686.

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Despite recent improvements in computer vision, artificial visual systems’ design is still daunting since an explanation of visual computing algorithms remains elusive. Salient object detection is one problem that is still open due to the difficulty of understanding the brain’s inner workings. Progress in this research area follows the traditional path of hand-made designs using neuroscience knowledge or, more recently, deep learning, a particular branch of machine learning. Recently, a different approach based on genetic programming appeared to enhance handcrafted techniques following two different strategies. The first method follows the idea of combining previous hand-made methods through genetic programming and fuzzy logic. The second approach improves the inner computational structures of basic hand-made models through artificial evolution. This research proposes expanding the artificial dorsal stream using a recent proposal based on symbolic learning to solve salient object detection problems following the second technique. This approach applies the fusion of visual saliency and image segmentation algorithms as a template. The proposed methodology discovers several critical structures in the template through artificial evolution. We present results on a benchmark designed by experts with outstanding results in an extensive comparison with the state of the art, including classical methods and deep learning approaches to highlight the importance of symbolic learning in visual saliency.

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Pigozzi, Federico, Eric Medvet et Laura Nenzi. « Mining Road Traffic Rules with Signal Temporal Logic and Grammar-Based Genetic Programming ». Applied Sciences 11, n^o 22 (10 novembre 2021) : 10573. http://dx.doi.org/10.3390/app112210573.

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Traffic systems, where human and autonomous drivers interact, are a very relevant instance of complex systems and produce behaviors that can be regarded as trajectories over time. Their monitoring can be achieved by means of carefully stated properties describing the expected behavior. Such properties can be expressed using Signal Temporal Logic (STL), a specification language for expressing temporal properties in a formal and human-readable way. However, manually authoring these properties is a hard task, since it requires mastering the language and knowing the system to be monitored. Moreover, in practical cases, the expected behavior is not known, but it has instead to be inferred from a set of trajectories obtained by observing the system. Often, those trajectories come devoid of human-assigned labels that can be used as an indication of compliance with expected behavior. As an alternative to manual authoring, automatic mining of STL specifications from unlabeled trajectories would enable the monitoring of autonomous agents without sacrificing human-readability. In this work, we propose a grammar-based evolutionary computation approach for mining the structure and the parameters of an STL specification from a set of unlabeled trajectories. We experimentally assess our approach on a real-world road traffic dataset consisting of thousands of vehicle trajectories. We show that our approach is effective at mining STL specifications that model the system at hand and are interpretable for humans. To the best of our knowledge, this is the first such study on a set of unlabeled real-world road traffic data. Being able to mine interpretable specifications from this kind of data may improve traffic safety, because mined specifications may be helpful for monitoring traffic and planning safety promotion strategies.

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HARA, AKIRA, et TOMOHARU NAGAO. « CONSTRUCTION AND ANALYSIS OF STOCK MARKET MODEL USING ADG : AUTOMATICALLY DEFINED GROUPS ». International Journal of Computational Intelligence and Applications 02, n^o 04 (décembre 2002) : 433–46. http://dx.doi.org/10.1142/s1469026802000749.

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In real market, the squares of stock price change rates have high autocorrelation, and the change rates show high peak and fat tail distribution. With the aim of analyzing the mechanism of the stock price change, we construct an artificial stock market composed of multiple agents whose investment strategies are represented by tree-shaped programs. The market is optimized by using a Genetic Programming so that the change of its stock price resembles that of "real" stock market statistically. In order to perform an efficient optimization and to analyze agents' behavior easily, we use ADG; Automatically Defined Groups previously proposed by authors. We show experimentally that complex changes such as real market appear in the proposed artificial market. Moreover we analyze the interaction of agents which causes realistic stock price changes.

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Mendyk, Aleksander, Sinan Güres, Renata Jachowicz, Jakub Szlęk, Sebastian Polak, Barbara Wiśniowska et Peter Kleinebudde. « From Heuristic to Mathematical Modeling of Drugs Dissolution Profiles : Application of Artificial Neural Networks and Genetic Programming ». Computational and Mathematical Methods in Medicine 2015 (2015) : 1–9. http://dx.doi.org/10.1155/2015/863874.

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The purpose of this work was to develop a mathematical model of the drug dissolution (Q) from the solid lipid extrudates based on the empirical approach. Artificial neural networks (ANNs) and genetic programming (GP) tools were used. Sensitivity analysis of ANNs provided reduction of the original input vector. GP allowed creation of the mathematical equation in two major approaches: (1) direct modeling ofQversus extrudate diameter (d) and the time variable (t) and (2) indirect modeling through Weibull equation. ANNs provided also information about minimum achievable generalization error and the way to enhance the original dataset used for adjustment of the equations’ parameters. Two inputs were found important for the drug dissolution:dandt. The extrudates length (L) was found not important. Both GP modeling approaches allowed creation of relatively simple equations with their predictive performance comparable to the ANNs (root mean squared error (RMSE) from 2.19 to 2.33). The direct mode of GP modeling ofQversusdandtresulted in the most robust model. The idea of how to combine ANNs and GP in order to escape ANNs’ black-box drawback without losing their superior predictive performance was demonstrated. Open Source software was used to deliver the state-of-the-art models and modeling strategies.

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Revelo Sánchez, Oscar, César Collazos Ordóñez et Miguel Redondo Duque. « Group formation in collaborative learning contexts based on personality traits : An empirical study in initial Programming courses ». Interaction Design and Architecture(s), n^o 49 (10 septembre 2021) : 29–45. http://dx.doi.org/10.55612/s-5002-049-002.

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Considering that group formation is one of the key processes when developing activities in collaborative learning contexts, this paper aims to propose a technique based on an approach of genetic algorithms to achieve homogeneous groups, considering the students' personality traits as grouping criteria. For its validation, an experiment was designed with 132 first semesters engineering students, quantifying their personality traits through the “Big Five Inventory”, forming workgroups and developing a collaborative activity in initial Programming courses. The experiment made it possible to compare the results obtained by the students applying the proposed approach to those obtained through other group formation strategies. It was demonstrated through the experiment that the homogeneous groups generated by the proposed technique produce better academic results compared to the grouping technique by students’ preference, traditionally used by the teachers when developing a collaborative activity.

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Li, Xijie, Ying Lv, Wei Sun et Li Zhou. « Cordon- or Link-Based Pricing : Environment-Oriented Toll Design Models Development and Application ». Sustainability 11, n^o 1 (7 janvier 2019) : 258. http://dx.doi.org/10.3390/su11010258.

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This study focuses on an environment-friendly toll design problem, where an acceptable road network performance is promised. First, a Traffic Performance Index (TPI)-based evaluation method is developed to help identify the optimal congestion level and the management target of a transportation system. Second, environment-oriented cordon- and link-based road toll design models are respectively proposed through the use of bi-level programming. Both upper-level submodel objectives are to minimize gross revenue (the total collected toll minus the emissions treatment cost) under different pricing strategies. Both lower-level submodels quantify the user equilibrium (UE) condition under elastic demand. Moreover, the TPI-related constraints for the management requirements of the network performance are incorporated into the bi-level programming modeling framework, which can lead to 0–1 mixed integer bi-level nonlinear programming for toll design problems. Accordingly, a genetic algorithm-based heuristic searching method is proposed for the two pricing models. The proposed cordon- and link-based pricing models were then applied to a real-world road network in Beijing, China. The effects of the toll schemes generated from the two models were compared in terms of emissions reduction and congestion mitigation. In this study, it was indicated that a higher total collected toll may lead to more emissions and related treatment costs. Tradeoffs existed between the toll scheme, emissions reduction, and congestion mitigation.

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Smirnov, Alexander, et Nikolay Teslya. « Selecting effective action strategies for the participants in a hospitalization process with the use of a fuzzy cooperative game and a genetic algorithm ». Information and Control Systems, n^o 2 (11 mai 2022) : 42–52. http://dx.doi.org/10.31799/1684-8853-2022-2-42-52.

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Introduction: The use of linear programming methods in making decisions on hospitalization in a fragile epidemiological situation may be hampered by the necessity to take account of a large number of parameters and limitations of the participants. Purpose: Development of an approach to selecting effective action strategies for the participants in a hospitalization process, with social factors taken into consideration. The approach is based on the theory of cooperative games which are solved with the use of a genetic algorithm. Results: A cost function has been developed for evaluating the effectiveness of the hospitalization process on the basis of the selected strategies and in consideration of social factors. A genetic algorithm has been designed in which the proposed effectiveness evaluation function is used as a fitness function for a population, while to determine chromosomes of individuals in the population the set of selected strategies of the hospitalization process participants is used. The approach has been tested using the data on hospitalizations of patients with suspected COVID-19, that were provided by several ambulance stations in Saint-Petersburg, Russia. The study shows the superiority of the proposed approach over the previously developed one in terms of the speed of solving a cooperative game, the quality of the solution being maintained. Practical relevance: Some software which is based on the proposed approach can be integrated into an ambulance dispatcher’s automated workstation to support decision-making during the process of hospitalization in a fragile epidemiological situation.

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Lones, Michael A. « Evolving continuous optimisers from scratch ». Genetic Programming and Evolvable Machines 22, n^o 4 (20 octobre 2021) : 395–428. http://dx.doi.org/10.1007/s10710-021-09414-8.

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AbstractThis work uses genetic programming to explore the space of continuous optimisers, with the goal of discovering novel ways of doing optimisation. In order to keep the search space broad, the optimisers are evolved from scratch using Push, a Turing-complete, general-purpose, language. The resulting optimisers are found to be diverse, and explore their optimisation landscapes using a variety of interesting, and sometimes unusual, strategies. Significantly, when applied to problems that were not seen during training, many of the evolved optimisers generalise well, and often outperform existing optimisers. This supports the idea that novel and effective forms of optimisation can be discovered in an automated manner. This paper also shows that pools of evolved optimisers can be hybridised to further increase their generality, leading to optimisers that perform robustly over a broad variety of problem types and sizes.

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Lum, Guo Zhan, Zhou Ye, Xiaoguang Dong, Hamid Marvi, Onder Erin, Wenqi Hu et Metin Sitti. « Shape-programmable magnetic soft matter ». Proceedings of the National Academy of Sciences 113, n^o 41 (26 septembre 2016) : E6007—E6015. http://dx.doi.org/10.1073/pnas.1608193113.

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Shape-programmable matter is a class of active materials whose geometry can be controlled to potentially achieve mechanical functionalities beyond those of traditional machines. Among these materials, magnetically actuated matter is particularly promising for achieving complex time-varying shapes at small scale (overall dimensions smaller than 1 cm). However, previous work can only program these materials for limited applications, as they rely solely on human intuition to approximate the required magnetization profile and actuating magnetic fields for their materials. Here, we propose a universal programming methodology that can automatically generate the required magnetization profile and actuating fields for soft matter to achieve new time-varying shapes. The universality of the proposed method can therefore inspire a vast number of miniature soft devices that are critical in robotics, smart engineering surfaces and materials, and biomedical devices. Our proposed method includes theoretical formulations, computational strategies, and fabrication procedures for programming magnetic soft matter. The presented theory and computational method are universal for programming 2D or 3D time-varying shapes, whereas the fabrication technique is generic only for creating planar beams. Based on the proposed programming method, we created a jellyfish-like robot, a spermatozoid-like undulating swimmer, and an artificial cilium that could mimic the complex beating patterns of its biological counterpart.

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Wang, Mingming, Li Wang, Xinyue Xu, Yong Qin et Lingqiao Qin. « Genetic Algorithm-Based Particle Swarm Optimization Approach to Reschedule High-Speed Railway Timetables : A Case Study in China ». Journal of Advanced Transportation 2019 (20 mars 2019) : 1–12. http://dx.doi.org/10.1155/2019/6090742.

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In this study, a mixed integer programming model is proposed to address timetable rescheduling problem under primary delays. The model considers timetable rescheduling strategies such as retiming, reordering, and adjusting stop pattern. A genetic algorithm-based particle swarm optimization algorithm is developed where position vector and genetic evolution operators are reconstructed based on departure and arrival time of each train at stations. Finally, a numerical experiment of Beijing-Shanghai high-speed railway corridor is implemented to test the proposed model and algorithm. The results show that the objective value of proposed method is decreased by 15.6%, 48.8%, and 25.7% compared with the first-come-first-service strategy, the first-schedule-first-service strategy, and the particle swarm optimization, respectively. The gap between the best solution obtained by the proposed method and the optimum solution computed by CPLEX solver is around 19.6%. All delay cases are addressed within acceptable time (within 1.5 min). Moreover, the case study gives insight into the correlation between delay propagation and headway. The primary delays occur in high-density period (scheduled headway closes to the minimum headway), which results in a great delay propagation.

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Rocholl, Jens, et Lars Mönch. « Hybrid algorithms for the earliness–tardiness single-machine multiple orders per job scheduling problem with a common due date ». RAIRO - Operations Research 52, n^o 4-5 (octobre 2018) : 1329–50. http://dx.doi.org/10.1051/ro/2018029.

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In this paper, we study an earliness–tardiness scheduling problem for a single machine that is motivated by process conditions found in semiconductor wafer fabrication facilities (wafer fabs). In modern 300-mm wafer fabs, front opening unified pods (FOUPs) transfer wafers. The number of FOUPs is limited to avoid a congestion of the Automated Material Handling System. Several orders can be grouped in one FOUP. A nonrestrictive common due date for all the orders is assumed. Only orders that belong to the same family can be processed together in a single FOUP at the same time. We present a Mixed Integer Linear Programming (MILP) formulation for this problem. Moreover, we show that this scheduling problem is NP-hard. We propose several simple heuristics based on dispatching rules and assignment strategies from bin packing. Moreover, genetic algorithms are designed that assign the orders to the set of early and tardy orders, respectively. In addition, a random key genetic algorithm (RKGA) is described that proposes order sequences. The different algorithms are hybridized with job formation and sequencing heuristics. A more specialized algorithm that is based on the generalized assignment problem is presented for the special case of a single order family. Results of computational experiments based on randomly generated problem instances are presented. They demonstrate that the genetic algorithms perform well with respect to solution quality and computing time under a broad range of experimental conditions.

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Dalchau, Neil, Matthew J. Smith, Samuel Martin, James R. Brown, Stephen Emmott et Andrew Phillips. « Towards the rational design of synthetic cells with prescribed population dynamics ». Journal of The Royal Society Interface 9, n^o 76 (8 juin 2012) : 2883–98. http://dx.doi.org/10.1098/rsif.2012.0280.

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The rational design of synthetic cell populations with prescribed behaviours is a long-standing goal of synthetic biology, with the potential to greatly accelerate the development of biotechnological applications in areas ranging from medical research to energy production. Achieving this goal requires well-characterized components, modular implementation strategies, simulation across temporal and spatial scales and automatic compilation of high-level designs to low-level genetic parts that function reliably inside cells. Many of these steps are incomplete or only partially understood, and methods for integrating them within a common design framework have yet to be developed. Here, we address these challenges by developing a prototype framework for designing synthetic cells with prescribed population dynamics. We extend the genetic engineering of cells (GEC) language, originally developed for programming intracellular dynamics, with cell population factors such as cell growth, division and dormancy, together with spatio-temporal simulation methods. As a case study, we use our framework to design synthetic cells with predator–prey interactions that, when simulated, produce complex spatio-temporal behaviours such as travelling waves and spatio-temporal chaos. An analysis of our design reveals that environmental factors such as density-dependent dormancy and reduced extracellular space destabilize the population dynamics and increase the range of genetic variants for which complex spatio-temporal behaviours are possible. Our findings highlight the importance of considering such factors during the design process. We then use our analysis of population dynamics to inform the selection of genetic parts, which could be used to obtain the desired spatio-temporal behaviours. By identifying, integrating and automating key stages of the design process, we provide a computational framework for designing synthetic systems, which could be tested in future laboratory studies.

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Spunde, Karina, Ksenija Korotkaja et Anna Zajakina. « Recombinant Viral Vectors for Therapeutic Programming of Tumour Microenvironment : Advantages and Limitations ». Biomedicines 10, n^o 9 (31 août 2022) : 2142. http://dx.doi.org/10.3390/biomedicines10092142.

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Viral vectors have been widely investigated as tools for cancer immunotherapy. Although many preclinical studies demonstrate significant virus-mediated tumour inhibition in synergy with immune checkpoint molecules and other drugs, the clinical success of viral vector applications in cancer therapy currently is limited. A number of challenges have to be solved to translate promising vectors to clinics. One of the key elements of successful virus-based cancer immunotherapy is the understanding of the tumour immune state and the development of vectors to modify the immunosuppressive tumour microenvironment (TME). Tumour-associated immune cells, as the main component of TME, support tumour progression through multiple pathways inducing resistance to treatment and promoting cancer cell escape mechanisms. In this review, we consider DNA and RNA virus vectors delivering immunomodulatory genes (cytokines, chemokines, co-stimulatory molecules, antibodies, etc.) and discuss how these viruses break an immunosuppressive cell development and switch TME to an immune-responsive “hot” state. We highlight the advantages and limitations of virus vectors for targeted therapeutic programming of tumour immune cell populations and tumour stroma, and propose future steps to establish viral vectors as a standard, efficient, safe, and non-toxic cancer immunotherapy approach that can complement other promising treatment strategies, e.g., checkpoint inhibitors, CAR-T, and advanced chemotherapeutics.

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Mao, Xinhua, Jibiao Zhou, Changwei Yuan et Dan Liu. « Resilience-Based Optimization of Postdisaster Restoration Strategy for Road Networks ». Journal of Advanced Transportation 2021 (12 février 2021) : 1–15. http://dx.doi.org/10.1155/2021/8871876.

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This work proposes a framework for the optimization of postdisaster road network restoration strategies from a perspective of resilience. The network performance is evaluated by the total system travel time (TSTT). After the implementation of a postdisaster restoration schedule, the network flows in a certain period of days are on a disequilibrium state; thus, a link-based day-to-day traffic assignment model is employed to compute TSTT and simulate the traffic evolution. Two indicators are developed to assess the road network resilience, i.e., the resilience of performance loss and the resilience of recovery rapidity. The former is calculated based on TSTT, and the latter is computed according to the restoration makespan. Then, we formulate the restoration optimization problem as a resilience-based bi-objective mixed integer programming model aiming to maximize the network resilience. Due to the NP-hardness of the model, a genetic algorithm is developed to solve the model. Finally, a case study is conducted to demonstrate the effectiveness of the proposed method. The effects of key parameters including the number of work crews, travelers’ sensitivity to travel time, availability of budget, and decision makers’ preference on the values of the two objectives are investigated as well.

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Hathwalia, Shruthi, et Naresh Grover. « Novel Design of a Digital PLL for Power Reduction ». International Journal of Online and Biomedical Engineering (iJOE) 18, n^o 07 (14 juin 2022) : 57–69. http://dx.doi.org/10.3991/ijoe.v18i07.30033.

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Very large-scale integration (VLSI) circuits operating at ultra-low power are currently acquiring more attention from the research group and the industries too. Recently, the performance of many applications depends on the size and energy consumption of sensor nodes. The energy efficient sensor nodes with reduced size are much preferred among applications such as wireless sensor networks, pollution and plant monitoring. The biggest challenge faced by the VLSI designers in present day life is designing a product of new generation which operates on minimum possible power. Power consideration is the eventual design criteria in different real-life applications like pacemakers etc. Phase Locked Loop is a framework that produces an output which is in phase with the input signal. In Phase Locked Loop (PLL), the focus is at the input signal’s phase with its output oscillator signal’s phase and thus modifies the recurrence frequency of the Voltage Controlled Oscillator (VCO) for phase co-ordination. The output signal from the Phase Frequency Detector is utilized to control the oscillator in a feedback circle also. As an operational gadget, the PLL has wide scope of utilization in media transmission, PCs and electronic applications. In this paper, designing and analysis of PLL with numerous outputs has been proposed to be implemented by changing the closed loop frequency control framework PLL blocks. As such, highly effective, low power, ideal area chip can be used for PLL with four various yields as PLL 8x, PLL 4x, PLL 2x and PLL 1x with different frequencies separately. Likewise, it is planned to utilize multi-threshold or sleep transistors to minimize the leakage current in the circuit. Further, the execution or performance verification of various parameters of PLL are done to acquire negligible power. Direct toolbox YALMIP is used as programming solver as an optimization tool. In addition to this, another method of Non- dominated sorting genetic algorithm (NSGA-II) Correlation for optimization is also introduced between both the strategies to find the best outcomes.

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Chandra Srivastava, Sharad, et Toran Verma. « Modelling And Optimization of Nonlinear Proportional Integral Controller ». Journal of Futuristic Sciences and Applications 2, n^o 2 (2019) : 25–30. http://dx.doi.org/10.51976/jfsa.221904.

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The NPIC (Nonlinear proportional integral Controller) has been studied in this research, and a genetic method has been used to find the smallest possible error. Integral absolute error was used to develop the objective function (IAE)A nonlinear component is included in this controller, making it one of a kind in its mix of proportional and integral control. For non-linear robotic manipulators, this means that the controller may be far more effective than a linear controller, which has historically been difficult to operate. Consequently, this controller offers a nonlinear controller for manipulators. Programming controller parameters to achieve high trajectory tracking has always been a difficult and time-consuming task for engineers, thus developing a system that can handle non-linearity and complexity has become more difficult in the next year. An intelligent controller is needed to meet today's demands It is the goal of this review article to provide an in-depth analysis of different controllers and control approaches as well as optimization strategies.

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Abraham, Ajith. « Business Intelligence from Web Usage Mining ». Journal of Information & ; Knowledge Management 02, n^o 04 (décembre 2003) : 375–90. http://dx.doi.org/10.1142/s0219649203000565.

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The rapid e-commerce growth has made both business community and customers face a new situation. Due to intense competition on the one hand and the customer's option to choose from several alternatives, the business community has realized the necessity of intelligent marketing strategies and relationship management. Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. Web usage mining has become very critical for effective Web site management, creating adaptive Web sites, business and support services, personalization, network traffic flow analysis and so on. This paper presents the important concepts of Web usage mining and its various practical applications. Further a novel approach called "intelligent-miner" (i-Miner) is presented. i-Miner could optimize the concurrent architecture of a fuzzy clustering algorithm (to discover web data clusters) and a fuzzy inference system to analyze the Web site visitor trends. A hybrid evolutionary fuzzy clustering algorithm is proposed to optimally segregate similar user interests. The clustered data is then used to analyze the trends using a Takagi-Sugeno fuzzy inference system learned using a combination of evolutionary algorithm and neural network learning. Proposed approach is compared with self-organizing maps (to discover patterns) and several function approximation techniques like neural networks, linear genetic programming and Takagi–Sugeno fuzzy inference system (to analyze the clusters). The results are graphically illustrated and the practical significance is discussed in detail. Empirical results clearly show that the proposed Web usage-mining framework is efficient.

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Martínez-Valderrama, Jaime, Javier Ibáñez, Francisco J. Alcalá et Silvio Martínez. « SAT : A Software for Assessing the Risk of Desertification in Spain ». Scientific Programming 2020 (29 juin 2020) : 1–12. http://dx.doi.org/10.1155/2020/7563928.

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Desertification is a major global environmental issue exacerbated by climate change. Strategies to combat desertification include prevention which seeks to reverse the process before the system reaches the stable desertified state. One of these initiatives is to implement early warning tools. This paper presents SAT (the Spanish acronym for Early Warning System), a decision support system (DSS), for assessing the risk of desertification in Spain, where 20% of the land has already been desertified and 1% is in active degradation. SAT relies on three versions of a Generic Desertification Model (GDM) that integrates economics and ecology under the predator-prey paradigm. The models have been programmed using Vensim, a type of software used to build and simulate System Dynamics (SD) models. Through Visual Basic programming, these models are operated from the Excel environment. In addition to the basic simulation exercises, specially designed tools have been coupled to assess the risk of desertification and determine the ranking of the most influential factors of the process. The users targeted by SAT are government land-use planners as well as desertification experts. SAT tool is implemented for five case studies, each one of them representing a desertification syndrome identified in Spain. Given the general nature of the tool and the fact that all United Nations Convention to Combat Desertification (UNCCD) signatory countries are committed to developing their National Plans to Combat Desertification (NPCD), SAT could be exported to regions threatened by desertification and expanded to cover more case studies.

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Zhou, Binghai, et Shi Zong. « Adaptive memory red deer algorithm for cross-dock truck scheduling with products time window ». Engineering Computations 38, n^o 8 (8 mars 2021) : 3254–89. http://dx.doi.org/10.1108/ec-05-2020-0273.

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Purpose The cross-docking strategy has a significant influence on supply chain and logistics efficiency. This paper aims to investigate the most suitable and efficient way to schedule the transfer of logistics activities and present a meta-heuristic method of the truck scheduling problem in cross-docking logistics. A truck scheduling problem with products time window is investigated with objectives of minimizing the total product transshipment time and earliness and tardiness cost of outbound trucks. Design/methodology/approach This research proposed a meta-heuristic method for the truck scheduling problem with products time window. To solve the problem, a lower bound of the problem is built through a novel two-stage Lagrangian relaxation problem and on account of the NP-hard nature of the truck scheduling problem, the novel red deer algorithm with the mechanism of the heuristic oscillating local search algorithm, as well as adaptive memory programming was proposed to overcome the inferior capability of the original red deer algorithm in the aspect of local search and run time. Findings Theory analysis and simulation experiments on an industrial case of a cross-docking center with a product’s time window are conducted in this paper. Satisfactory results show that the performance of the red deer algorithm is enhanced due to the mechanism of heuristic oscillating local search algorithm and adaptive memory programming and the proposed method efficiently solves the real-world size case of truck scheduling problems in cross-docking with product time window. Research limitations/implications The consideration of products time window has very realistic significance in different logistics applications such as cold-chain logistics and pharmaceutical supply chain. Furthermore, the novel adaptive memory red deer algorithm could be modified and applied to other complex optimization scheduling problems such as scheduling problems considering energy-efficiency or other logistics strategies. Originality/value For the first time in the truck scheduling problem with the cross-docking strategy, the product’s time window is considered. Furthermore, a mathematical model with objectives of minimizing the total product transshipment time and earliness and tardiness cost of outbound trucks is developed. To solve the proposed problem, a novel adaptive memory red deer algorithm with the mechanism of heuristic oscillating local search algorithm was proposed to overcome the inferior capability of genetic algorithm in the aspect of local search and run time.

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Janssen, Joseph A. M. J. L. « New Insights into the Role of Insulin and Hypothalamic-Pituitary-Adrenal (HPA) Axis in the Metabolic Syndrome ». International Journal of Molecular Sciences 23, n^o 15 (25 juillet 2022) : 8178. http://dx.doi.org/10.3390/ijms23158178.

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Recent data suggests that (pre)diabetes onset is preceded by a period of hyperinsulinemia. Consumption of the “modern” Western diet, over-nutrition, genetic background, decreased hepatic insulin clearance, and fetal/metabolic programming may increase insulin secretion, thereby causing chronic hyperinsulinemia. Hyperinsulinemia is an important etiological factor in the development of metabolic syndrome, type 2 diabetes, cardiovascular disease, polycystic ovarian syndrome, and Alzheimer’s disease. Recent data suggests that the onset of prediabetes and diabetes are preceded by a variable period of hyperinsulinemia. Emerging data suggest that chromic hyperinsulinemia is also a driving force for increased activation of the hypothalamic-adrenal-pituitary (HPA) axis in subjects with the metabolic syndrome, leading to a state of “functional hypercortisolism”. This “functional hypercortisolism” by antagonizing insulin actions may prevent hypoglycemia. It also disturbs energy balance by shifting energy fluxes away from muscles toward abdominal fat stores. Synergistic effects of hyperinsulinemia and “functional hypercortisolism” promote abdominal visceral obesity and insulin resistance which are core pathophysiological components of the metabolic syndrome. It is hypothesized that hyperinsulinemia-induced increased activation of the HPA axis plays an important etiological role in the development of the metabolic syndrome and its consequences. Numerous studies have demonstrated reversibility of hyperinsulinemia with lifestyle, surgical, and pharmaceutical-based therapies. Longitudinal studies should be performed to investigate whether strategies that reduce hyperinsulinemia at an early stage are successfully in preventing increased activation of the HPA axis and the metabolic syndrome.

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Castelletti, A., F. Pianosi, X. Quach et R. Soncini-Sessa. « Assessing water reservoirs management and development in Northern Vietnam ». Hydrology and Earth System Sciences 16, n^o 1 (23 janvier 2012) : 189–99. http://dx.doi.org/10.5194/hess-16-189-2012.

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Abstract. In many developing countries water is a key renewable resource to complement carbon-emitting energy production and support food security in the face of demand pressure from fast-growing industrial production and urbanization. To cope with undergoing changes, water resources development and management have to be reconsidered by enlarging their scope across sectors and adopting effective tools to analyze current and projected infrastructure potential and operation strategies. In this paper we use multi-objective deterministic and stochastic optimization to assess the current reservoir operation and planned capacity expansion in the Red River Basin (Northern Vietnam), and to evaluate the potential improvement by the adoption of a more sophisticated information system. To reach this goal we analyze the historical operation of the major controllable infrastructure in the basin, the HoaBinh reservoir on the Da River, explore re-operation options corresponding to different tradeoffs among the three main objectives (hydropower production, flood control and water supply), using multi-objective optimization techniques, namely Multi-Objective Genetic Algorithm. Finally, we assess the structural system potential and the need for capacity expansion by application of Deterministic Dynamic Programming. Results show that the current operation can only be relatively improved by advanced optimization techniques, while investment should be put into enlarging the system storage capacity and exploiting additional information to inform the operation.

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Anatskaya, Olga V., et Alexander E. Vinogradov. « Polyploidy as a Fundamental Phenomenon in Evolution, Development, Adaptation and Diseases ». International Journal of Molecular Sciences 23, n^o 7 (24 mars 2022) : 3542. http://dx.doi.org/10.3390/ijms23073542.

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DNA replication during cell proliferation is ‘vertical’ copying, which reproduces an initial amount of genetic information. Polyploidy, which results from whole-genome duplication, is a fundamental complement to vertical copying. Both organismal and cell polyploidy can emerge via premature cell cycle exit or via cell-cell fusion, the latter giving rise to polyploid hybrid organisms and epigenetic hybrids of somatic cells. Polyploidy-related increase in biological plasticity, adaptation, and stress resistance manifests in evolution, development, regeneration, aging, oncogenesis, and cardiovascular diseases. Despite the prevalence in nature and importance for medicine, agri- and aquaculture, biological processes and epigenetic mechanisms underlying these fundamental features largely remain unknown. The evolutionarily conserved features of polyploidy include activation of transcription, response to stress, DNA damage and hypoxia, and induction of programs of morphogenesis, unicellularity, and longevity, suggesting that these common features confer adaptive plasticity, viability, and stress resistance to polyploid cells and organisms. By increasing cell viability, polyploidization can provide survival under stressful conditions where diploid cells cannot survive. However, in somatic cells it occurs at the expense of specific function, thus promoting developmental programming of adult cardiovascular diseases and increasing the risk of cancer. Notably, genes arising via evolutionary polyploidization are heavily involved in cancer and other diseases. Ploidy-related changes of gene expression presumably originate from chromatin modifications and the derepression of bivalent genes. The provided evidence elucidates the role of polyploidy in evolution, development, aging, and carcinogenesis, and may contribute to the development of new strategies for promoting regeneration and preventing cardiovascular diseases and cancer.

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Quan, Xibin, Hao Xie, Xinye Wang, Jubing Zhang, Jiayu Wei, Zhicong Zhang et Meijing Liu. « Optimization and Performance Analysis of a Distributed Energy System Considering the Coordination of the Operational Strategy and the Fluctuation of Annual Hourly Load ». Applied Sciences 12, n^o 19 (21 septembre 2022) : 9449. http://dx.doi.org/10.3390/app12199449.

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The operation strategies of a distributed energy system (DES) are usually proposed according to the electrical load (FEL) and the thermal load (FTL), which take the cooling/heating load or electric load as unique constraint conditions that result in a too high or too low equipment load rate. This paper proposes a new hybrid operation strategy (HOS) that takes the full utilization of natural gas and the minimization of power consumption from the power grid as constraints and coordinates the cooling/electricity ratio and heating/electricity ratio of buildings and equipment. In the optimization phase of a DES, an optimization method based on the discretization of the load is proposed to investigate the influence of the uncertainty of the load on the DES, which helps to avoid repeated load simulations and provides stronger adjustability by quoting the normal distribution function to obtain multiple sets of load data with different fluctuations. Further, a multi-objective optimization model combining the genetic algorithm (GA) and mixed integer linear programming algorithm (MILP) was established to find the optimal configuration of equipment capacities by comprehensively considering the annual total cost, carbon emissions, and energy efficiency of the DES. Finally, an office building example was used to validate the feasibility of the above theories and methods. Compared with the FEL and FTL, the HOS reduced the energy waste of the DES by 19.7% and 15.5%, respectively. Compared with using a typical daily load, using an annual hourly load to optimize the DES-HOS produced a better comprehensive performance and lower adverse impacts derived from load fluctuations.

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Fava, Leandro, João Furtado, Gilson Helfer, Jorge Barbosa, Marko Beko, Sérgio Correia et Valderi Leithardt. « A Multi-Start Algorithm for Solving the Capacitated Vehicle Routing Problem with Two-Dimensional Loading Constraints ». Symmetry 13, n^o 9 (14 septembre 2021) : 1697. http://dx.doi.org/10.3390/sym13091697.

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This work presents a multistart algorithm for solving the capacitated vehicle routing problem with 2D loading constraints (2L-CVRP) allowing for the rotation of goods. Research dedicated to graph theory and symmetry considered the vehicle routing problem as a classical application. This problem has complex aspects that stimulate the use of advanced algorithms and symmetry in graphs. The use of graph modeling of the 2L-CVRP problem by undirected graph allowed the high performance of the algorithm. The developed algorithm is based on metaheuristics, such as the Constructive Genetic Algorithm (CGA) to construct promising initial solutions; a Tabu Search (TS) to improve the initial solutions on the routing problem, and a Large Neighborhood Search (LNS) for the loading subproblem. Although each one of these algorithms allowed to solve parts of the 2L-CVRP, the combination of these three algorithms to solve this problem was unprecedented in the scientific literature. In our approach, a parallel mechanism for checking the loading feasibility of routes was implemented using multithreading programming to improve the performance. Additionally, memory structures such as hash-tables were implemented to save time by storing and querying previously evaluated results for the loading feasibility of routes. For benchmarks, tests were done on well-known instances available in the literature. The results proved that the framework matched or outperformed most of the previous approaches. As the main contribution, this work brings higher quality solutions for large-size instances of the pure CVRP. This paper involves themes related to the symmetry journal, mainly complex algorithms, graphs, search strategies, complexity, graph modeling, and genetic algorithms. In addition, the paper especially focuses on topic-related aspects of special interest to the community involved in symmetry studies, such as graph algorithms and graph theory.

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Cheaitou, Ali, Sadeque Hamdan et Rim Larbi. « Liner shipping network design with sensitive demand ». Maritime Business Review 6, n^o 3 (1 février 2021) : 293–313. http://dx.doi.org/10.1108/mabr-10-2019-0045.

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Purpose This paper aims to examine containership routing and speed optimization for maritime liner services. It focuses on a realistic case in which the transport demand, and consequently the collected revenue from the visited ports depend on the sailing speed. Design/methodology/approach The authors present an integer non-linear programming model for the containership routing and fleet sizing problem, in which the sailing speed of every leg, the ports to be included in the service and their sequence are optimized based on the net line's profit. The authors present a heuristic approach that is based on speed discretization and a genetic algorithm to solve the problem for large size instances. They present an application on a line provided by COSCO in 2017 between Asia and Europe. Findings The numerical results show that the proposed heuristic approach provides good quality solutions after a reasonable computation time. In addition, the demand sensitivity has a great impact on the selected route and therefore the profit function. Moreover, the more the demand is sensitive to the sailing speed, the higher the sailing speed value. Research limitations/implications The vessel carrying capacity is not considered in an explicit way. Originality/value This paper focuses on an important aspect in liner shipping, i.e. demand sensitivity to sailing speed. It brings a novel approach that is important in a context in which sailing speed strategies and market volatility are to be considered together in network design. This perspective has not been addressed previously.

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Miss. Sushmita H. Kshirsagar et al. « Review on : Multi - Objective optimization of Composite Automobile over Drive Shaft ». International Journal on Recent and Innovation Trends in Computing and Communication 7, n^o 3 (22 mars 2019) : 44–47. http://dx.doi.org/10.17762/ijritcc.v7i3.5260.

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Composites is increasingly used in different applications in the last decade, especially in aerospace due to their high strength and lightweight characteristics. Indeed, the latest models of Airbus (A350) and Boeing (B787) have employed more than 50 wt% of composites, mainly Carbon Fiber Reinforced Polymers (CFRP). Yet, the increased use of CFRP has raised the environmental concerns about their end-of-life related to waste disposal, consumption of non-renewable resources for manufacturing and the need to recycle CFRP wastes. In this study, generic model is developed in order to propose an optimal management of aerospace CFRP wastes taking into account economic and environmental objectives. Initially, a life-cycle systemic approach is used to model the environmental impacts of CFRP recycling processes focusing on Global Warming Potential (GWP) following the guidelines of Life Cycle Assessment (LCA). The whole supply chain for recycling CFRP pathways is then modeled from aircraft dismantling sites to the reuse of recycled fibers in various applications. A multiobjective optimization strategy based on mathematical programming, ε-constraint and lexicographic methods with appropriate decision-making techniques (M-TOPSIS, PROMETHEE-GAIA) has been developed to determine CFRP waste supply chain configurations. Different scenarios have been studied in order to take account the potential of existing recycling sites in a mono-period visions as well as the deployment of new sites in a multi-period approach considering the case study of France for illustration purpose. The solutions obtained from optimization process allow developing optimal strategies for the implementation of CFRP recovery with recycled fibers (of acceptable quality) for the targeted substitution use while minimizing cost /maximizing profit for an economic criterion and minimizing an environmental impact based on GWP.

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Wang, Xiaoshu, et Masanori Sugisaka. « Learning soccer strategies by Genetic Programming ». Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 2000 (5 mai 2000) : 205–9. http://dx.doi.org/10.5687/sss.2000.205.

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Sette, S., et L. Boullart. « Genetic programming : principles and applications ». Engineering Applications of Artificial Intelligence 14, n^o 6 (décembre 2001) : 727–36. http://dx.doi.org/10.1016/s0952-1976(02)00013-1.

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Gielen, C. « Genetic programming ». Neurocomputing 6, n^o 1 (février 1994) : 120–22. http://dx.doi.org/10.1016/0925-2312(94)90038-8.

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Chengqi Zhang*, Ling Guan** et Zheru Chi. « Introduction to the Special Issue on Learning in Intelligent Algorithms and Systems Design ». Journal of Advanced Computational Intelligence and Intelligent Informatics 3, n^o 6 (20 décembre 1999) : 439–40. http://dx.doi.org/10.20965/jaciii.1999.p0439.

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Learning has long been and will continue to be a key issue in intelligent algorithms and systems design. Emulating the behavior and mechanisms of human learning by machines at such high levels as symbolic processing and such low levels as neuronal processing has long been a dominant interest among researchers worldwide. Neural networks, fuzzy logic, and evolutionary algorithms represent the three most active research areas. With advanced theoretical studies and computer technology, many promising algorithms and systems using these techniques have been designed and implemented for a wide range of applications. This Special Issue presents seven papers on learning in intelligent algorithms and systems design from researchers in Japan, China, Australia, and the U.S. Neural Networks: Emulating low-level human intelligent processing, or neuronal processing, gave birth of artificial neural networks more than five decades ago. It was hoped that devices based on biological neural networks would possess characteristics of the human brain. Neural networks have reattracted researchers' attention since the late 1980s when back-propagation algorithms were used to train multilayer feed-forward neural networks. In the last decades, we have seen promising progress in this research field yield many new models, learning algorithms, and real-world applications, evidenced by the publication of new journals in this field. Fuzzy Logic: Since L. A. Zadeh introduced fuzzy set theory in 1965, fuzzy logic has increasingly become the focus of many researchers and engineers opening up new research and problem solving. Fuzzy set theory has been favorably applied to control system design. In the last few years, fuzzy model applications have bloomed in image processing and pattern recognition. Evolutionary Algorithms: Evolutionary optimization algorithms have been studied over three decades, emulating natural evolutionary search and selection so powerful in global optimization. The study of evolutionary algorithms includes evolutionary programming (EP), evolutionary strategies (ESs), genetic algorithms (GAs), and genetic programming (GP). In the last few years, we have also seen multiple computational algorithms combined to maximize system performance, such as neurofuzzy networks, fuzzy neural networks, fuzzy logic and genetic optimization, neural networks, and evolutionary algorithms. This Special Issue also includes papers that introduce combined techniques. Wang et al present an improved fuzzy algorithm for enhanced eyeground images. Examination of the eyeground image is effective in diagnosing glaucoma and diabetes. Conventional eyeground image quality is usually too poor for doctors to obtain useful information, so enhancement is required to eliminate this. Due to details and uncertainties in eyeground images, conventional enhancement such as histogram equalization, edge enhancement, and high-pass filters fail to achieve good results. Fuzzy enhancement enhances images in three steps: (1) transferring an image from the spatial domain to the fuzzy domain; (2) conducting enhancement in the fuzzy domain; and (3) returning the image from the fuzzy domain to the spatial domain. The paper detailing this proposes improved mapping and fast implementation. Mohammadian presents a method for designing self-learning hierarchical fuzzy logic control systems based on the integration of evolutionary algorithms and fuzzy logic. The purpose of such an approach is to provide an integrated knowledge base for intelligent control and collision avoidance in a multirobot system. Evolutionary algorithms are used as in adaptation for learning fuzzy knowledge bases of control systems and learning, mapping, and interaction between fuzzy knowledge bases of different fuzzy logic systems. Fuzzy integral has been found useful in data fusion. Pham and Wagner present an approach based on the fuzzy integral and GAs to combine likelihood values of cohort speakers. The fuzzy integral nonlinearly fuses similarity measures of an utterance assigned to cohort speakers. In their approach, Gas find optimal fuzzy densities required for fuzzy fusion. Experiments using commercial speech corpus T146 show their approach achieves more favorable performance than conventional normalization. Evolution reflects the behavior of a society. Puppala and Sen present a coevolutionary approach to generating behavioral strategies for cooperating agent groups. Agent behavior evolves via GAs, where one genetic algorithm population is evolved per individual in the cooperative group. Groups are evaluated by pairing strategies from each population and best strategy pairs are stored together in shared memory. The approach is evaluated using asymmetric room painting and results demonstrate the superiority of shared memory over random pairing in consistently generating optimal behavior patterns. Object representation and template optimization are two main factors affecting object recognition performance. Lu et al present an evolutionary algorithm for optimizing handwritten numeral templates represented by rational B-spline surfaces of character foreground-background-distance distribution maps. Initial templates are extracted from training a feed-forward neural network instead of using arbitrarily chosen patterns to reduce iterations required in evolutionary optimization. To further reduce computational complexity, a fast search is used in selection. Using 1,000 optimized numeral templates, the classifier achieves a classification rate of 96.4% while rejecting 90.7% of nonnumeral patterns when tested on NIST Special Database 3. Determining an appropriate number of clusters is difficult yet important. Li et al based their approach based on rival penalized competitive learning (RPCL), addressing problems of overlapped clusters and dependent components of input vectors by incorporating full covariance matrices into the original RPCL algorithm. The resulting learning algorithm progressively eliminates units whose clusters contain only a small amount of training data. The algorithm is applied to determine the number of clusters in a Gaussian mixture distribution and to optimize the architecture of elliptical function networks for speaker verification and for vowel classification. Another important issue on learning is Kurihara and Sugawara's adaptive reinforcement learning algorithm integrating exploitation- and exploration-oriented learning. This algorithm is more robust in dynamically changing, large-scale environments, providing better performance than either exploitation- learning or exploration-oriented learning, making it is well suited for autonomous systems. In closing we would like to thank the authors who have submitted papers to this Special Issue and express our appreciation to the referees for their excellent work in reading papers under a tight schedule.

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Boonsothonsatit, Ganda. « Generic decision support system to leverage supply chain performance (GLE) for SMEs in Thailand ». Journal of Manufacturing Technology Management 28, n^o 6 (3 juillet 2017) : 737–48. http://dx.doi.org/10.1108/jmtm-02-2017-0029.

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Purpose This paper explains the development stages of a generic decision support system to leverage supply chain performance (GLE). The purpose of this paper is to identify and trade off the critical supply chain measures which are interrelated and in contradiction with each other. Design/methodology/approach The GLE was developed as an extension of the supply chain performance assessment tool proposed by Banomyong and Supatn (2011). It contained nine measures covering key activities along the supply chain under dimensions of cost, time and reliability. Their interrelations were figured out by causal linkages, whereas their contradictions were traded off as multi-objective optimization. It is solved using fuzzy goal programming along with a weighted max-min operator in order to acquire the Pareto-optimal solution. Findings The results from the GLE showed there were two critical supply chain measures including supply chain cost per sales and average order cycle time. They contradictorily influenced by a root-cause, namely product lot size. Its Pareto-optimal value was provided to achieve the minimized values of supply chain cost per sales and average order cycle time which were consistent with their relative weights. Research limitations/implications As generic features, the GLE needs further validation in several industries under various supply chain strategies. The further validation may contribute the GLE to include multiple decision variables, multiple types of product and multiple periods of time. In addition, the GLE may consider a dimensional measure of environmental impact along the supply chain activities. Originality/value The GLE is a unique decision support system to identify and trade off the critical, interrelated and contradicting supply chain measures. More uniqueness is obtained when the GLE offers an option of inputting a set of relative weights for the interrelated supply chain measures.

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Liu, Xiaodong, Hongqiang Guo, Xingqun Cheng, Juan Du et Jian Ma. « A Robust Design of the Model-Free-Adaptive-Control-Based Energy Management for Plug-in Hybrid Electric Vehicle ». Energies 15, n^o 20 (11 octobre 2022) : 7467. http://dx.doi.org/10.3390/en15207467.

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This paper proposes a robust design approach based on the Design for Six Sigma (DFSS), to promote the robustness of our previous model-free-adaptive-control-based (MFAC-based) energy management strategy (EMS) for the plug-in hybrid electric vehicles (PHEVs) in real-time application. First, the multi-island genetic algorithm (MIGA) is employed for a deterministic design of the MFAC-based EMS, and the Monte Carlo simulation (MCS) is utilized to evaluate the sigma level of the strategy with the deterministic design results. Second, a DFSS framework is formulated to reinforce the robustness of the MFAC-based EMS, in which the velocity and the vehicle mass are considered external disturbances whilst the terminal state of charge (SOC) of the battery and the fuel consumption (FC) are conducted as responses. In addition, real-time SOC constraints are incorporated into Pontryagin’s minimum principle (PMP) to confine the fluctuation of battery SOC in MFAC-based EMS to make it closer to the solution of the dynamic programming (DP). Finally, the effectiveness of the robust design results is assessed by contrasting with other strategies for various combined driving cycles (including velocity, vehicle mass, and road slope). The comparisons demonstrate the remarkable promotion of the robust design in terms of the energy-saving potential and the performance against external disturbance. The average improvement of the FCs can reach up to a considerable 19.66% and 9.79% in contrast to the charge-depleting and charge-sustaining (CD-CS) strategy as well as the deterministic design of MFAC-based EMS. In particular, the energy-saving performance is comparable to DP, where there is only a gap of −1.68%.

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Belotti, Pietro, Christian Kirches, Sven Leyffer, Jeff Linderoth, James Luedtke et Ashutosh Mahajan. « Mixed-integer nonlinear optimization ». Acta Numerica 22 (2 avril 2013) : 1–131. http://dx.doi.org/10.1017/s0962492913000032.

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Many optimal decision problems in scientific, engineering, and public sector applications involve both discrete decisions and nonlinear system dynamics that affect the quality of the final design or plan. These decision problems lead to mixed-integer nonlinear programming (MINLP) problems that combine the combinatorial difficulty of optimizing over discrete variable sets with the challenges of handling nonlinear functions. We review models and applications of MINLP, and survey the state of the art in methods for solving this challenging class of problems.Most solution methods for MINLP apply some form of tree search. We distinguish two broad classes of methods: single-tree and multitree methods. We discuss these two classes of methods first in the case where the underlying problem functions are convex. Classical single-tree methods include nonlinear branch-and-bound and branch-and-cut methods, while classical multitree methods include outer approximation and Benders decomposition. The most efficient class of methods for convex MINLP are hybrid methods that combine the strengths of both classes of classical techniques.Non-convex MINLPs pose additional challenges, because they contain non-convex functions in the objective function or the constraints; hence even when the integer variables are relaxed to be continuous, the feasible region is generally non-convex, resulting in many local minima. We discuss a range of approaches for tackling this challenging class of problems, including piecewise linear approximations, generic strategies for obtaining convex relaxations for non-convex functions, spatial branch-and-bound methods, and a small sample of techniques that exploit particular types of non-convex structures to obtain improved convex relaxations.We finish our survey with a brief discussion of three important aspects of MINLP. First, we review heuristic techniques that can obtain good feasible solution in situations where the search-tree has grown too large or we require real-time solutions. Second, we describe an emerging area of mixed-integer optimal control that adds systems of ordinary differential equations to MINLP. Third, we survey the state of the art in software for MINLP.

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Sharma, Divya, et Shikha Lohchab. « Search based Software Modularization Using Evolution Algorithm ». NeuroQuantology 20, n^o 5 (18 mai 2022) : 822–31. http://dx.doi.org/10.14704/nq.2022.20.5.nq22240.

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To comprehend a Software system, Software modularization strategies are used. The goal of modularization is to break down a software system into meaningful and intelligible sub-systems from its source-code (modules). Because software classification modularization is an NP-hard task, evolutionary methods produce better modularization quality rather than avaricious algorithms. All available transformative techniques for software modularization only take into account structural aspects reliant on programming language syntax. Because most computer languages lack a mechanism for extracting structural characteristics, they cannot be modularized. A novel heuristic is proposed in this work. with several objectives that accomplishes both in order to lead optimization algorithms towards a proper decomposition of software systems automatically, structural (e.g.; calling dependence and in-heritance dependency) and non- structural (e.g.; semantics in code comments and identifier names) aspects are used. It is analyzed using 3 optimization plans, viz; global-based-search, combining global and local search, and Estimation of Distribution (EoD) to upgrade it. According to outcomes on Mozilla Firefox, suggested optimization algorithm based on EoD and the newly developed MOF function exceed those so it use structural-based objective functions in finding more understandable modules, as well as guiding the optimization procedure. In the lack of a unique concept, structure, the original design can be identified by using the source code of the disturbed software. Effective software maintenance depends on the concept of software system. One of most powerful techniques in software clustering is the ability to divide enormous Software systems into workable subsystems with modules of identical characteristics, thereby reducing the complexity of the system. A metaheuristic optimization imperialist competitive system has emerged algorithm, genetic algorithm, and their combination is examined for software clustering in this paper. When it comes to value, of clustering, the number of epochs required for convergence, and the standard unconventionality found at the end of repeated application of these algorithms, it appears that recursive application is the most effective for achieving the best performance.

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Ciesielski, Vic. « Linear genetic programming ». Genetic Programming and Evolvable Machines 9, n^o 1 (15 août 2007) : 105–6. http://dx.doi.org/10.1007/s10710-007-9036-8.

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Worzel, William P., Jianjun Yu, Arpit A. Almal et Arul M. Chinnaiyan. « Applications of genetic programming in cancer research ». International Journal of Biochemistry & ; Cell Biology 41, n^o 2 (février 2009) : 405–13. http://dx.doi.org/10.1016/j.biocel.2008.09.025.

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Langdon, W. B., et W. Banzhaf. « Repeated patterns in genetic programming ». Natural Computing 7, n^o 4 (26 mai 2007) : 589–613. http://dx.doi.org/10.1007/s11047-007-9038-8.

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Giot, Romain, et Christophe Rosenberger. « Genetic programming for multibiometrics ». Expert Systems with Applications 39, n^o 2 (février 2012) : 1837–47. http://dx.doi.org/10.1016/j.eswa.2011.08.066.

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