Proposes the application of a genetic algorithm (GA) and simulated annealing (SA) based hybrid approach for the scheduling of generator maintenance in power systems using an integer representation. The adapted approach uses the probabilistic acceptance criterion of simulated annealing within the genetic algorithm framework. A case study is formulated in this paper as an integer programming problem using a reliability-based objective function and typical problem constraints. The implementation and performance of the solution technique are discussed. The results in this paper demonstrate that the technique is more effective than approaches based solely on genetic algorithms or solely on simulated annealing. It therefore proves to be a valid approach for the solution of generator maintenance scheduling problems.
{"title":"GA/SA-based hybrid techniques for the scheduling of generator maintenance in power systems","authors":"K. Dahal, G. Burt, J. McDonald, S. Galloway","doi":"10.1109/CEC.2000.870347","DOIUrl":"https://doi.org/10.1109/CEC.2000.870347","url":null,"abstract":"Proposes the application of a genetic algorithm (GA) and simulated annealing (SA) based hybrid approach for the scheduling of generator maintenance in power systems using an integer representation. The adapted approach uses the probabilistic acceptance criterion of simulated annealing within the genetic algorithm framework. A case study is formulated in this paper as an integer programming problem using a reliability-based objective function and typical problem constraints. The implementation and performance of the solution technique are discussed. The results in this paper demonstrate that the technique is more effective than approaches based solely on genetic algorithms or solely on simulated annealing. It therefore proves to be a valid approach for the solution of generator maintenance scheduling problems.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130155451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Plant scheduling and planning are two of the most important decision-making problems in manufacturing industry. In general, these two decision-making problems are complex, due to the features of combinatorial nature for production-strategy selection and coupling properties for constrained requirements. In this paper, we have developed two general mixed-integer nonlinear programming models to formulate the scheduling and planning problems. In order to obtain a global solution, mixed-integer hybrid differential evolution with a multiplier updating method is introduced to solve both constrained problems. The proposed method can use parameters to obtain a feasible solution as compared with the penalty function approach.
{"title":"Plant scheduling and planning using mixed-integer hybrid differential evolution with multiplier updating","authors":"Yung-Chien Lin, Kao-Shing Hwang, Feng-Sheng Wang","doi":"10.1109/CEC.2000.870351","DOIUrl":"https://doi.org/10.1109/CEC.2000.870351","url":null,"abstract":"Plant scheduling and planning are two of the most important decision-making problems in manufacturing industry. In general, these two decision-making problems are complex, due to the features of combinatorial nature for production-strategy selection and coupling properties for constrained requirements. In this paper, we have developed two general mixed-integer nonlinear programming models to formulate the scheduling and planning problems. In order to obtain a global solution, mixed-integer hybrid differential evolution with a multiplier updating method is introduced to solve both constrained problems. The proposed method can use parameters to obtain a feasible solution as compared with the penalty function approach.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114511869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Investigates the use of partial functions and fitness sharing in genetic programming. Fitness sharing is applied to populations of either partial or total functions and the results are compared. Applications to two classes of problem are investigated: learning multiplexer definitions, and learning (recursive) list membership functions. In both cases, fitness sharing approaches outperform the use of raw fitness, by generating more accurate solutions with the same population parameters. On the list membership problem, variants using fitness sharing on populations of partial functions outperform variants using total functions, whereas populations of total functions give better performance on some variants of multiplexer problems.
{"title":"Partial functions in fitness-shared genetic programming","authors":"R. I. McKay","doi":"10.1109/CEC.2000.870316","DOIUrl":"https://doi.org/10.1109/CEC.2000.870316","url":null,"abstract":"Investigates the use of partial functions and fitness sharing in genetic programming. Fitness sharing is applied to populations of either partial or total functions and the results are compared. Applications to two classes of problem are investigated: learning multiplexer definitions, and learning (recursive) list membership functions. In both cases, fitness sharing approaches outperform the use of raw fitness, by generating more accurate solutions with the same population parameters. On the list membership problem, variants using fitness sharing on populations of partial functions outperform variants using total functions, whereas populations of total functions give better performance on some variants of multiplexer problems.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116371672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To self-adapt ([Schwefel, 1981], [Fogel et al., 1991]) a search parameter, rather than fixing the parameter globally before search begins the value is encoded in each individual along with the other genes. This is done in the hope that the value will then become adapted on a per-individual basis. While this mechanism is very powerful and in some cases essential to achieving good search performance, the dynamics of the adaptation of such traits are often complex and difficult to predict. This paper presents a case study in which self-adapting mutation rates were found to quickly drop below the threshold of effectiveness, bringing productive search to a premature halt. We identify three conditions that may in practice lead to such premature convergence of self-adapting mutation rates. The third condition is of particular interest, involving an interaction between self-adaptation and a process referred to here as "implicit self-adaptation". Our investigation ultimately underlines a key aspect of population-based search: namely, how strongly search is directed toward finding solutions that are not just of high quality, but those which also produce other high quality solutions when subjected to the chosen variation process.
为了自适应([Schwefel, 1981]， [Fogel et al.， 1991])搜索参数，而不是在搜索开始前全局固定参数，该值与其他基因一起编码在每个个体中。这样做的目的是希望该值能够在每个人的基础上进行调整。虽然这种机制非常强大，并且在某些情况下对于实现良好的搜索性能至关重要，但这些特征的适应动态通常是复杂且难以预测的。本文提出了一个案例研究，其中发现自适应突变率迅速下降到有效性阈值以下，使生产性搜索过早停止。我们确定了可能在实践中导致这种自适应突变率过早收敛的三个条件。第三个条件特别有趣，涉及自我适应和这里称为“内隐自我适应”的过程之间的相互作用。我们的调查最终强调了基于人群的搜索的一个关键方面:即，搜索是如何强烈地指向寻找解决方案，不仅是高质量的，而且那些也产生其他高质量的解决方案，当受到选择的变化过程。
{"title":"Reasons for premature convergence of self-adapting mutation rates","authors":"Matthew R. Glickman, K. Sycara","doi":"10.1109/CEC.2000.870276","DOIUrl":"https://doi.org/10.1109/CEC.2000.870276","url":null,"abstract":"To self-adapt ([Schwefel, 1981], [Fogel et al., 1991]) a search parameter, rather than fixing the parameter globally before search begins the value is encoded in each individual along with the other genes. This is done in the hope that the value will then become adapted on a per-individual basis. While this mechanism is very powerful and in some cases essential to achieving good search performance, the dynamics of the adaptation of such traits are often complex and difficult to predict. This paper presents a case study in which self-adapting mutation rates were found to quickly drop below the threshold of effectiveness, bringing productive search to a premature halt. We identify three conditions that may in practice lead to such premature convergence of self-adapting mutation rates. The third condition is of particular interest, involving an interaction between self-adaptation and a process referred to here as \"implicit self-adaptation\". Our investigation ultimately underlines a key aspect of population-based search: namely, how strongly search is directed toward finding solutions that are not just of high quality, but those which also produce other high quality solutions when subjected to the chosen variation process.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134478307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An online map building evolutionary algorithm is proposed using multi-agent mobile robots with odometric uncertainty. The control algorithm for map building in each robot is identical and trained by an online evolutionary algorithm (EA). Each robot has configuration uncertainty which increases as it moves, and it perceives the surrounding environment information by the limited range sensors. It communicates with other robots and shares the information. The elementary behaviors are defined and they are used to build a map. EA is applied to the defined behavior set for optimizing the robot actions. To demonstrate the effectiveness of the proposed algorithm, computer simulations are conducted for various environments.
{"title":"Online map building evolutionary algorithm for multi-agent mobile robots with odometric uncertainty","authors":"Yong-Jae Kim, Jong-Hwan Kim","doi":"10.1109/CEC.2000.870286","DOIUrl":"https://doi.org/10.1109/CEC.2000.870286","url":null,"abstract":"An online map building evolutionary algorithm is proposed using multi-agent mobile robots with odometric uncertainty. The control algorithm for map building in each robot is identical and trained by an online evolutionary algorithm (EA). Each robot has configuration uncertainty which increases as it moves, and it perceives the surrounding environment information by the limited range sensors. It communicates with other robots and shares the information. The elementary behaviors are defined and they are used to build a map. EA is applied to the defined behavior set for optimizing the robot actions. To demonstrate the effectiveness of the proposed algorithm, computer simulations are conducted for various environments.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133355240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Coevolution has been used as optimization technique both successfully and unsuccessfully. Successful optimization shows integration of information at the individual level over many fitness evaluation events and over many generations. Alternative outcomes of the evolutionary process, e.g. red queen dynamics or speciation, prevent such integration. Why coevolution leads to integration of information or to alternative evolutionary outcomes is generally unclear. We study coevolutionary optimization of the density classification task in cellular automata in a spatially explicit, two-species model. We find optimization at the individual level, i.e. evolution of cellular automata that are good density classifiers. However, when we globally mix the populations, which prevents the formation of spatial patterns, we find typical red queen dynamics in which cellular automata classify all cases to a single density class regardless their actual density. Thus, we get different outcomes of the evolutionary process dependent on a small change in the model. We compare the two processes leading to the different outcomes in terms of the diversity of the two populations at the level of the genotype and at the level of the phenotype.
{"title":"Information integration and red queen dynamics in coevolutionary optimization","authors":"Ludo Pagie, P. Hogeweg","doi":"10.1109/CEC.2000.870795","DOIUrl":"https://doi.org/10.1109/CEC.2000.870795","url":null,"abstract":"Coevolution has been used as optimization technique both successfully and unsuccessfully. Successful optimization shows integration of information at the individual level over many fitness evaluation events and over many generations. Alternative outcomes of the evolutionary process, e.g. red queen dynamics or speciation, prevent such integration. Why coevolution leads to integration of information or to alternative evolutionary outcomes is generally unclear. We study coevolutionary optimization of the density classification task in cellular automata in a spatially explicit, two-species model. We find optimization at the individual level, i.e. evolution of cellular automata that are good density classifiers. However, when we globally mix the populations, which prevents the formation of spatial patterns, we find typical red queen dynamics in which cellular automata classify all cases to a single density class regardless their actual density. Thus, we get different outcomes of the evolutionary process dependent on a small change in the model. We compare the two processes leading to the different outcomes in terms of the diversity of the two populations at the level of the genotype and at the level of the phenotype.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129329604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bayesian evolutionary algorithms (BEAs) are a probabilistic model of evolutionary computation for learning and optimization. Starting from a population of individuals drawn from a prior distribution, a Bayesian evolutionary algorithm iteratively generates a new population by estimating the posterior fitness distribution of parent individuals and then sampling from the distribution offspring individuals by variation and selection operators. Due to the non-homogeneity of their Markov chains, the convergence properties of the full BEAs are difficult to analyze. However, recent developments in Markov chain analysis for dynamic Monte Carlo methods provide a useful tool for studying asymptotic behaviors of adaptive Markov chain Monte Carlo methods including evolutionary algorithms. We apply these results to Investigate the convergence properties of Bayesian evolutionary algorithms with incremental data growth. We study the case of BEAs that generate single chains or have populations of size one. It is shown that under regularity conditions the incremental BEA asymptotically converges to a maximum a posteriori (MAP) estimate which is concentrated around the maximum likelihood estimate. This result relies on the observation that increasing the number of data items has an equivalent effect of reducing the temperature in simulated annealing.
{"title":"Convergence properties of incremental Bayesian evolutionary algorithms with single Markov chains","authors":"Byoung-Tak Zhang, G. Paass, H. Mühlenbein","doi":"10.1109/CEC.2000.870744","DOIUrl":"https://doi.org/10.1109/CEC.2000.870744","url":null,"abstract":"Bayesian evolutionary algorithms (BEAs) are a probabilistic model of evolutionary computation for learning and optimization. Starting from a population of individuals drawn from a prior distribution, a Bayesian evolutionary algorithm iteratively generates a new population by estimating the posterior fitness distribution of parent individuals and then sampling from the distribution offspring individuals by variation and selection operators. Due to the non-homogeneity of their Markov chains, the convergence properties of the full BEAs are difficult to analyze. However, recent developments in Markov chain analysis for dynamic Monte Carlo methods provide a useful tool for studying asymptotic behaviors of adaptive Markov chain Monte Carlo methods including evolutionary algorithms. We apply these results to Investigate the convergence properties of Bayesian evolutionary algorithms with incremental data growth. We study the case of BEAs that generate single chains or have populations of size one. It is shown that under regularity conditions the incremental BEA asymptotically converges to a maximum a posteriori (MAP) estimate which is concentrated around the maximum likelihood estimate. This result relies on the observation that increasing the number of data items has an equivalent effect of reducing the temperature in simulated annealing.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129379160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The vehicle routing problem with time windows (VRPTW) is a very important problem in the transportation industry since it occurs frequently in everyday practice, e.g. in scheduling bank deliveries. Many heuristic algorithms have been proposed for this NP-hard problem. This paper reports the successful application of GrEVeRT (Graph-based Evolutionary algorithm for the Vehicle Routing Problem with Time windows), an evolutionary algorithm based on a directed acyclic graph model. On well-known benchmark instances of the VRPTW, we obtain better results than those reported by other researchers using genetic algorithms.
带时间窗的车辆路径问题(VRPTW)是交通运输行业中一个非常重要的问题，因为它在日常实践中经常发生，例如安排银行交货。针对这个np困难问题，已经提出了许多启发式算法。本文报道了基于有向无环图模型的进化算法GrEVeRT (graph -based evolution algorithm for the Vehicle Routing Problem with Time window)的成功应用。在已知的VRPTW基准实例上，我们获得了比其他研究人员使用遗传算法报道的更好的结果。
{"title":"Evolving schedule graphs for the vehicle routing problem with time windows","authors":"H. Ozdemir, C. Mohan","doi":"10.1109/CEC.2000.870734","DOIUrl":"https://doi.org/10.1109/CEC.2000.870734","url":null,"abstract":"The vehicle routing problem with time windows (VRPTW) is a very important problem in the transportation industry since it occurs frequently in everyday practice, e.g. in scheduling bank deliveries. Many heuristic algorithms have been proposed for this NP-hard problem. This paper reports the successful application of GrEVeRT (Graph-based Evolutionary algorithm for the Vehicle Routing Problem with Time windows), an evolutionary algorithm based on a directed acyclic graph model. On well-known benchmark instances of the VRPTW, we obtain better results than those reported by other researchers using genetic algorithms.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123815272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Object avoidance is a fundamental task of autonomous, mobile robots. For this task, the pertinent literature proposes various architectures, which vary from simple Braitenberg vehicles to camera-lens systems inspired by the compound eyes of insects. Due to certain hardware limitations, existing research resorts to prespecified sensor systems that remain fixed during all experiments and does modifications only in the software components of the controllers. By contrast, this paper is about the direct evolution of an artificial compound eye in hardware. The hardware consists of a particular robot that is able to autonomously modify the angular positions of 16 light sensors. Even though first experiments have been successful in evolving some solutions by means of evolutionary algorithms, they have also indicated that systematic comparisons between different evolutionary algorithms and codings schemes are required in order to speed up the evolutionary process. This paper summarizes some comparative simulation studies and validates their achievements on a physical robot. It turns out that these simulation studies can help to drastically improve the evolution of the eye's morphology with respect to both convergence speed and robustness if certain critical simulation parameters (e.g., noise level) are adopted from the physical robot.
{"title":"The evolution of an artificial compound eye by using adaptive hardware","authors":"L. Lichtensteiger, R. Salomon","doi":"10.1109/CEC.2000.870777","DOIUrl":"https://doi.org/10.1109/CEC.2000.870777","url":null,"abstract":"Object avoidance is a fundamental task of autonomous, mobile robots. For this task, the pertinent literature proposes various architectures, which vary from simple Braitenberg vehicles to camera-lens systems inspired by the compound eyes of insects. Due to certain hardware limitations, existing research resorts to prespecified sensor systems that remain fixed during all experiments and does modifications only in the software components of the controllers. By contrast, this paper is about the direct evolution of an artificial compound eye in hardware. The hardware consists of a particular robot that is able to autonomously modify the angular positions of 16 light sensors. Even though first experiments have been successful in evolving some solutions by means of evolutionary algorithms, they have also indicated that systematic comparisons between different evolutionary algorithms and codings schemes are required in order to speed up the evolutionary process. This paper summarizes some comparative simulation studies and validates their achievements on a physical robot. It turns out that these simulation studies can help to drastically improve the evolution of the eye's morphology with respect to both convergence speed and robustness if certain critical simulation parameters (e.g., noise level) are adopted from the physical robot.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125011009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A fuzzy logic controller (FLC) for mobile robots is designed in a hierarchical structure. The designed FLC consists of two levels: the planner level and the motion control level. The planner level generates a path to the destination with obstacle avoidance. The singleton outputs of the planner are obtained using line and arc methods. The lower motion control level calculates the robot's wheel velocity so as to follow the path generated by the planner as to the current robot posture. The fuzzy singleton outputs are obtained by heuristics and tuned by evolutionary programming. The applicability of the controller is demonstrated using a robot soccer system.
{"title":"Evolutionary programming-based fuzzy logic path planner and follower for mobile robots","authors":"Moon-Su Lee, M. Jung, Jong-Hwan Kim","doi":"10.1109/CEC.2000.870287","DOIUrl":"https://doi.org/10.1109/CEC.2000.870287","url":null,"abstract":"A fuzzy logic controller (FLC) for mobile robots is designed in a hierarchical structure. The designed FLC consists of two levels: the planner level and the motion control level. The planner level generates a path to the destination with obstacle avoidance. The singleton outputs of the planner are obtained using line and arc methods. The lower motion control level calculates the robot's wheel velocity so as to follow the path generated by the planner as to the current robot posture. The fuzzy singleton outputs are obtained by heuristics and tuned by evolutionary programming. The applicability of the controller is demonstrated using a robot soccer system.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124111506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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