Experimental results reported in many papers suggest that making an appropriate a priori choice of an evolutionary method for a nonlinear parameter optimisation problem remains an open question. It seems that the most promising approach at this stage of research is experimental, involving a design of a scalable test suite of constrained optimisation problems, in which many features could be easily tuned. Then it would be possible to evaluate merits and drawbacks of the available methods as well as test new methods efficiently. We discuss a new test-case generator for constrained parameter optimisation techniques, which deals with deficiencies of generators proposed earlier. This generator TCG-2 is capable of creating various test problems with different characteristics, including the dimensionality of the problem, number of local optima, number of active constraints at the optimum, topology of the feasible search space, etc. Such a test-case generator is very useful for analysing and comparing different constraint-handling techniques and different nonlinear parameter optimisation techniques.
{"title":"Test-case generator TCG-2 for nonlinear parameter optimisation","authors":"Martin Schmidt, Z. Michalewicz","doi":"10.1109/CEC.2000.870370","DOIUrl":"https://doi.org/10.1109/CEC.2000.870370","url":null,"abstract":"Experimental results reported in many papers suggest that making an appropriate a priori choice of an evolutionary method for a nonlinear parameter optimisation problem remains an open question. It seems that the most promising approach at this stage of research is experimental, involving a design of a scalable test suite of constrained optimisation problems, in which many features could be easily tuned. Then it would be possible to evaluate merits and drawbacks of the available methods as well as test new methods efficiently. We discuss a new test-case generator for constrained parameter optimisation techniques, which deals with deficiencies of generators proposed earlier. This generator TCG-2 is capable of creating various test problems with different characteristics, including the dimensionality of the problem, number of local optima, number of active constraints at the optimum, topology of the feasible search space, etc. Such a test-case generator is very useful for analysing and comparing different constraint-handling techniques and different nonlinear parameter optimisation techniques.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"330 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122476896","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}
Designing control systems using multiobjective genetic algorithms can lead to a substantial computational load as a result of the repeated evaluation of the multiple objectives and the population-based nature of the search. A neural network approach, based on radial basis functions, is introduced to alleviate this problem by providing computationally inexpensive estimates of objective values during the search. A straightforward example demonstrates the utility of the approach.
{"title":"Accelerating multi-objective control system design using a neuro-genetic approach","authors":"N. M. Duarte, A. Ruano, C. Fonseca, P. Fleming","doi":"10.1109/CEC.2000.870322","DOIUrl":"https://doi.org/10.1109/CEC.2000.870322","url":null,"abstract":"Designing control systems using multiobjective genetic algorithms can lead to a substantial computational load as a result of the repeated evaluation of the multiple objectives and the population-based nature of the search. A neural network approach, based on radial basis functions, is introduced to alleviate this problem by providing computationally inexpensive estimates of objective values during the search. A straightforward example demonstrates the utility of the approach.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126790682","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}
Considers the problem of scheduling rounds of a non-professional table-tennis league. We formalize the problem in terms of a timetabling optimization problem, then we solve this highly constrained problem with a permutation-based genetic algorithm for which feasibility-preserving operators are defined. Since coding and operators cannot warrant feasibility in every case, the fitness function penalizes constraint violations. This algorithm is compared to an even more elaborated variant, which additionally aims at repairing infeasible solutions produced by the genetic operators.
{"title":"Automated timetable generation for rounds of a table-tennis league","authors":"Jörn Schönberger, D. Mattfeld, H. Kopfer","doi":"10.1109/CEC.2000.870307","DOIUrl":"https://doi.org/10.1109/CEC.2000.870307","url":null,"abstract":"Considers the problem of scheduling rounds of a non-professional table-tennis league. We formalize the problem in terms of a timetabling optimization problem, then we solve this highly constrained problem with a permutation-based genetic algorithm for which feasibility-preserving operators are defined. Since coding and operators cannot warrant feasibility in every case, the fitness function penalizes constraint violations. This algorithm is compared to an even more elaborated variant, which additionally aims at repairing infeasible solutions produced by the genetic operators.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"50 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":"126097360","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}
Recently, the evolutionary robotics (ER) approach has been attracting lots of concern in the fields of robotics and artificial life, since it can automatically synthesize controllers by taking the embodiment and the interaction dynamics between the robot and its environment. However, the ER approach still has serious problems that have to be solved. In this study, we particularly focus on one of the critical problems in the ER: plasticity vs. stability dilemma. In order to alleviate this problem, we investigate the effectiveness of the dynamically-rearranging neural networks by taking a peg-collecting task, which requires appropriate sequence of behavior to accomplish the task, as a practical example.
{"title":"Evolutionary construction of behavior arbitration mechanisms based on dynamically-rearranging neural networks","authors":"H. Nakamura, A. Ishiguro, Y. Uchilkawa","doi":"10.1109/CEC.2000.870290","DOIUrl":"https://doi.org/10.1109/CEC.2000.870290","url":null,"abstract":"Recently, the evolutionary robotics (ER) approach has been attracting lots of concern in the fields of robotics and artificial life, since it can automatically synthesize controllers by taking the embodiment and the interaction dynamics between the robot and its environment. However, the ER approach still has serious problems that have to be solved. In this study, we particularly focus on one of the critical problems in the ER: plasticity vs. stability dilemma. In order to alleviate this problem, we investigate the effectiveness of the dynamically-rearranging neural networks by taking a peg-collecting task, which requires appropriate sequence of behavior to accomplish the task, as a practical example.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"74 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":"126174235","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}
In order to show that the parallel co-evolution of different heuristic methods may lead to an efficient search strategy, we have hybridized three heuristic agents of complementary behaviours: A Tabu Search is used as the main search algorithm, a Genetic Algorithm is in charge of the diversification and a Kick Operator is applied to intensify the search. The three agents run simultaneously, they communicate and cooperate via an adaptive memory which contains a history of the search already done, focusing on high quality regions of the search space. This paper presents CO-SEARCH, the co-evolving heuristic we have designed, and its application on large scale instances of the quadratic assignment problem. The evaluations have been executed on large scale network of workstations via a parallel environment which supports fault tolerance and adaptive dynamic scheduling of tasks.
{"title":"COSEARCH: a co-evolutionary metaheuristic","authors":"V. Bachelet, E. Talbi","doi":"10.1109/CEC.2000.870839","DOIUrl":"https://doi.org/10.1109/CEC.2000.870839","url":null,"abstract":"In order to show that the parallel co-evolution of different heuristic methods may lead to an efficient search strategy, we have hybridized three heuristic agents of complementary behaviours: A Tabu Search is used as the main search algorithm, a Genetic Algorithm is in charge of the diversification and a Kick Operator is applied to intensify the search. The three agents run simultaneously, they communicate and cooperate via an adaptive memory which contains a history of the search already done, focusing on high quality regions of the search space. This paper presents CO-SEARCH, the co-evolving heuristic we have designed, and its application on large scale instances of the quadratic assignment problem. The evaluations have been executed on large scale network of workstations via a parallel environment which supports fault tolerance and adaptive dynamic scheduling of tasks.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"102 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":"126924418","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 representation of candidate solutions and the variation operators are fundamental design choices in an evolutionary algorithm (EA). This paper proposes a novel representation technique and suitable variation operators for the degree-constrained minimum spanning tree problem. For a weighted, undirected graph G(V, E), this problem seeks to identify the shortest spanning tree whose node degrees do not exceed an upper bound d/spl ges/2. Within the EA, a candidate spanning tree is simply represented by its set of edges. Special initialization, crossover, and mutation operators are used to generate new, always feasible candidate solutions. In contrast to previous spanning tree representations, the proposed approach provides substantially higher locality and is nevertheless computationally efficient; an offspring is always created in O(|V|) time. In addition, it is shown how problem-dependent heuristics can be effectively incorporated into the initialization, crossover, and mutation operators without increasing the time-complexity. Empirical results are presented for hard problem instances with up to 500 vertices. Usually, the new approach identifies solutions superior to those of several other optimization methods within few seconds. The basic ideas of this EA are also applicable to other network optimization tasks.
{"title":"An efficient evolutionary algorithm for the degree-constrained minimum spanning tree problem","authors":"G. Raidl","doi":"10.1109/CEC.2000.870282","DOIUrl":"https://doi.org/10.1109/CEC.2000.870282","url":null,"abstract":"The representation of candidate solutions and the variation operators are fundamental design choices in an evolutionary algorithm (EA). This paper proposes a novel representation technique and suitable variation operators for the degree-constrained minimum spanning tree problem. For a weighted, undirected graph G(V, E), this problem seeks to identify the shortest spanning tree whose node degrees do not exceed an upper bound d/spl ges/2. Within the EA, a candidate spanning tree is simply represented by its set of edges. Special initialization, crossover, and mutation operators are used to generate new, always feasible candidate solutions. In contrast to previous spanning tree representations, the proposed approach provides substantially higher locality and is nevertheless computationally efficient; an offspring is always created in O(|V|) time. In addition, it is shown how problem-dependent heuristics can be effectively incorporated into the initialization, crossover, and mutation operators without increasing the time-complexity. Empirical results are presented for hard problem instances with up to 500 vertices. Usually, the new approach identifies solutions superior to those of several other optimization methods within few seconds. The basic ideas of this EA are also applicable to other network optimization tasks.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"231 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":"116394929","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 new evolutionary search algorithm, called BGP (Building-block approach to Genetic Programming), to be used for classification tasks in data mining, is introduced. It is different from existing evolutionary techniques in that it does not use indirect representations of a solution, such as bit strings or grammars. The algorithm uses decision trees of various sizes as individuals in the populations and operators, e.g. crossover, are performed directly on the trees. When compared to the C4.5 and CN2 induction algorithms on a benchmark set of problems, BGP shows very good results.
介绍了一种新的进化搜索算法，称为BGP (Building-block approach to Genetic Programming)，用于数据挖掘中的分类任务。它与现有的进化技术的不同之处在于，它不使用解决方案的间接表示，例如位字符串或语法。该算法使用不同大小的决策树作为种群中的个体，并直接在树上执行交叉等操作。在一组基准问题上与C4.5和CN2归纳算法进行比较，BGP显示出非常好的结果。
{"title":"Searching the forest: using decision trees as building blocks for evolutionary search in classification databases","authors":"S. Rouwhorst, A. Engelbrecht","doi":"10.1109/CEC.2000.870357","DOIUrl":"https://doi.org/10.1109/CEC.2000.870357","url":null,"abstract":"A new evolutionary search algorithm, called BGP (Building-block approach to Genetic Programming), to be used for classification tasks in data mining, is introduced. It is different from existing evolutionary techniques in that it does not use indirect representations of a solution, such as bit strings or grammars. The algorithm uses decision trees of various sizes as individuals in the populations and operators, e.g. crossover, are performed directly on the trees. When compared to the C4.5 and CN2 induction algorithms on a benchmark set of problems, BGP shows very good results.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"1 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":"128574171","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 fast method of GP based model building for time series prediction is proposed. The method involves two newly-devised techniques. One is regarding determination of model parameters: only functional forms are inherited from their parents with genetic programming, but model parameters are not inherited. They are optimized by a backpropagation-like algorithm when a child (model) is newborn. The other is regarding mutation: nodes which require a different number of edges, can be transformed into different types of nodes through mutation. This operation is effective at accelerating complicated functions e.g. seismic ground motion. The method has been applied to a typical benchmark of time series and many real world problems.
{"title":"GP-based modeling method for time series prediction with parameter optimization and node alternation","authors":"I. Yoshihara, T. Aoyama, M. Yasunaga","doi":"10.1109/CEC.2000.870828","DOIUrl":"https://doi.org/10.1109/CEC.2000.870828","url":null,"abstract":"A fast method of GP based model building for time series prediction is proposed. The method involves two newly-devised techniques. One is regarding determination of model parameters: only functional forms are inherited from their parents with genetic programming, but model parameters are not inherited. They are optimized by a backpropagation-like algorithm when a child (model) is newborn. The other is regarding mutation: nodes which require a different number of edges, can be transformed into different types of nodes through mutation. This operation is effective at accelerating complicated functions e.g. seismic ground motion. The method has been applied to a typical benchmark of time series and many real world problems.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"25 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":"130342216","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 basic evolutionary programming (BEP) method utilizes individual parent fitness to generate offspring. This is objectionable in many optimization problems, where the fitness value grows rapidly with problem dimensions, and two optimization problems differ by simply a scale factor. This paper is concerned with the development of an evolutionary programming method, which is functionally, and structurally equivalent to BEP, but still can be used effectively to optimize functions having strong fitness dependency between parents and their offspring. In this paper, a fitness-blind mutation (FBM) algorithm has been proposed, and then this is used in conjunction with the BEP mutation operator. The FBM operation has been implemented by taking the standard deviation of the Gaussian variable to vary in proportion to the genotypic distance between the individual parent and the fittest individual, which is defined as a pseudo-global optimum individual in a population pool. Also, the directionality of the random variation has been exploited to improve the probability of getting better solutions. In addition to this, the importance of initial search width for generating the offspring has been established empirically. The effectiveness of the proposed algorithm has been verified on well-established test functions.
{"title":"A novel hybrid evolutionary programming method for function optimization","authors":"A. Swain, A. Morris","doi":"10.1109/CEC.2000.870366","DOIUrl":"https://doi.org/10.1109/CEC.2000.870366","url":null,"abstract":"The basic evolutionary programming (BEP) method utilizes individual parent fitness to generate offspring. This is objectionable in many optimization problems, where the fitness value grows rapidly with problem dimensions, and two optimization problems differ by simply a scale factor. This paper is concerned with the development of an evolutionary programming method, which is functionally, and structurally equivalent to BEP, but still can be used effectively to optimize functions having strong fitness dependency between parents and their offspring. In this paper, a fitness-blind mutation (FBM) algorithm has been proposed, and then this is used in conjunction with the BEP mutation operator. The FBM operation has been implemented by taking the standard deviation of the Gaussian variable to vary in proportion to the genotypic distance between the individual parent and the fittest individual, which is defined as a pseudo-global optimum individual in a population pool. Also, the directionality of the random variation has been exploited to improve the probability of getting better solutions. In addition to this, the importance of initial search width for generating the offspring has been established empirically. The effectiveness of the proposed algorithm has been verified on well-established test functions.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"49 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":"129234768","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}
We present four abstract evolutionary algorithms for multi-objective optimization and theoretical results that characterize their convergence behavior. Thanks to these results it is easy to verify whether or not a particular instantiation of these abstract evolutionary algorithms offers the desired limit behavior. Several examples are given.
{"title":"Convergence properties of some multi-objective evolutionary algorithms","authors":"G. Rudolph, Alexandru Agapie","doi":"10.1109/CEC.2000.870756","DOIUrl":"https://doi.org/10.1109/CEC.2000.870756","url":null,"abstract":"We present four abstract evolutionary algorithms for multi-objective optimization and theoretical results that characterize their convergence behavior. Thanks to these results it is easy to verify whether or not a particular instantiation of these abstract evolutionary algorithms offers the desired limit behavior. Several examples are given.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"63 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":"124200383","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：