Pub Date : 2002-05-12DOI: 10.1109/CEC.2002.1004476
J. Vesterstrom, J. Riget, T. Krink
We introduce Division of Labor (DoL) from social insects to improve local optimisation of the Particle Swarm Optimiser (PSO). We compared the performance with the basic PSO, a GA and simulated annealing and found improvements around local optima. The PSO with DoL outperforms the basic PSO on most testcases and is comparable in local optimisation with SA.
{"title":"Division of labor in particle swarm optimisation","authors":"J. Vesterstrom, J. Riget, T. Krink","doi":"10.1109/CEC.2002.1004476","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004476","url":null,"abstract":"We introduce Division of Labor (DoL) from social insects to improve local optimisation of the Particle Swarm Optimiser (PSO). We compared the performance with the basic PSO, a GA and simulated annealing and found improvements around local optima. The PSO with DoL outperforms the basic PSO on most testcases and is comparable in local optimisation with SA.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124737227","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}
Pub Date : 2002-05-12DOI: 10.1109/CEC.2002.1004431
K. Tan, A. Tay, Tong-heng Lee, C. M. Heng
Genetic programming (GP) has emerged as a promising approach to deal with the classification task in data mining. This paper extends the tree representation of GP to evolve multiple comprehensible IF-THEN classification rules. We introduce a concept mapping technique for the fitness evaluation of individuals. A covering algorithm that employs an artificial immune system-like memory vector is utilized to produce multiple rules as well as to remove redundant rules. The proposed GP classifier is validated on nine benchmark data sets, and the simulation results confirm the viability and effectiveness of the GP approach for solving data mining problems in a wide spectrum of application domains.
{"title":"Mining multiple comprehensible classification rules using genetic programming","authors":"K. Tan, A. Tay, Tong-heng Lee, C. M. Heng","doi":"10.1109/CEC.2002.1004431","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004431","url":null,"abstract":"Genetic programming (GP) has emerged as a promising approach to deal with the classification task in data mining. This paper extends the tree representation of GP to evolve multiple comprehensible IF-THEN classification rules. We introduce a concept mapping technique for the fitness evaluation of individuals. A covering algorithm that employs an artificial immune system-like memory vector is utilized to produce multiple rules as well as to remove redundant rules. The proposed GP classifier is validated on nine benchmark data sets, and the simulation results confirm the viability and effectiveness of the GP approach for solving data mining problems in a wide spectrum of application domains.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124910823","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}
Pub Date : 2002-05-12DOI: 10.1109/CEC.2002.1006283
B. Al-kazemi, C. Mohan
Multi-phase particle swarm optimization is a new algorithm to be used for discrete and continuous problems. In this algorithm, different groups of particles have trajectories that proceed with differing goals in different phases of the algorithm. On several benchmark problems, the algorithm outperforms standard particle swarm optimization, genetic algorithm, and evolution programming.
{"title":"Multi-phase generalization of the particle swarm optimization algorithm","authors":"B. Al-kazemi, C. Mohan","doi":"10.1109/CEC.2002.1006283","DOIUrl":"https://doi.org/10.1109/CEC.2002.1006283","url":null,"abstract":"Multi-phase particle swarm optimization is a new algorithm to be used for discrete and continuous problems. In this algorithm, different groups of particles have trajectories that proceed with differing goals in different phases of the algorithm. On several benchmark problems, the algorithm outperforms standard particle swarm optimization, genetic algorithm, and evolution programming.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131996801","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}
Pub Date : 2002-05-12DOI: 10.1109/CEC.2002.1004546
S. Oeda, T. Icmmura, T. Yamashita
Artificial immune systems have been identified as artificially intelligent systems. Some algorithms have been developed on this antigen-antibody response. Here, a model is presented wherein the behavior of each immune cell is specified. We improve this model using knowledge of the major histocompatibility complex. For this purpose an evolutionary neural network was used. Qualitative analysis of the results offers verification of the effectiveness of this approach to simulating an immune system.
{"title":"Biological immune system by evolutionary adaptive learning of neural networks","authors":"S. Oeda, T. Icmmura, T. Yamashita","doi":"10.1109/CEC.2002.1004546","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004546","url":null,"abstract":"Artificial immune systems have been identified as artificially intelligent systems. Some algorithms have been developed on this antigen-antibody response. Here, a model is presented wherein the behavior of each immune cell is specified. We improve this model using knowledge of the major histocompatibility complex. For this purpose an evolutionary neural network was used. Qualitative analysis of the results offers verification of the effectiveness of this approach to simulating an immune system.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131567781","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}
Evolutionary programming has been applied to many optimization problems. However, on some function optimization problems its convergence rate is slow. In this paper, swarm directions are embedded in fast evolutionary programming. The swarm direction for an individual supplies its place to be mutated. The experimental results show its effectiveness and efficiency.
{"title":"Swarm directions embedded in fast evolutionary programming","authors":"Chengjian Wei, Zhenya He, Yifeng Zhang, Wenjiang Pei","doi":"10.1109/CEC.2002.1004427","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004427","url":null,"abstract":"Evolutionary programming has been applied to many optimization problems. However, on some function optimization problems its convergence rate is slow. In this paper, swarm directions are embedded in fast evolutionary programming. The swarm direction for an individual supplies its place to be mutated. The experimental results show its effectiveness and efficiency.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132423279","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}
Pub Date : 2002-05-12DOI: 10.1109/CEC.2002.1004479
Morten Løvbjerg, T. Krink
Particle swarm optimisers (PSOs) show potential in function optimisation, but still have room for improvement. Self-organized criticality (SOC) can help control the PSO and add diversity. Extending the PSO with SOC seems promising reaching faster convergence and better solutions.
{"title":"Extending particle swarm optimisers with self-organized criticality","authors":"Morten Løvbjerg, T. Krink","doi":"10.1109/CEC.2002.1004479","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004479","url":null,"abstract":"Particle swarm optimisers (PSOs) show potential in function optimisation, but still have room for improvement. Self-organized criticality (SOC) can help control the PSO and add diversity. Extending the PSO with SOC seems promising reaching faster convergence and better solutions.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133731373","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}
Pub Date : 2002-05-12DOI: 10.1109/CEC.2002.1004456
J. Isaacs, Robert K. Watkins, S. Foo
Using a genetic algorithm (GA) to evolve ant colony systems (ACS), we have succeeded at producing evolvable random number generators (RNG) that can be written to hardware. Although the simulated behavior of individual ants is limited to a small number of choices, "fit" colonies pass many stringent tests for randomness.
{"title":"Evolving ant colony systems in hardware for random number generation","authors":"J. Isaacs, Robert K. Watkins, S. Foo","doi":"10.1109/CEC.2002.1004456","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004456","url":null,"abstract":"Using a genetic algorithm (GA) to evolve ant colony systems (ACS), we have succeeded at producing evolvable random number generators (RNG) that can be written to hardware. Although the simulated behavior of individual ants is limited to a small number of choices, \"fit\" colonies pass many stringent tests for randomness.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133782058","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}
Pub Date : 2002-05-12DOI: 10.1109/CEC.2002.1006225
Sibylle D. Müller, N. Schraudolph, P. Koumoutsakos
We discuss the implementation of a learning algorithm for determining adaptation parameters in evolution strategies. As an initial test case, we consider the application of reinforcement learning for determining the relationship between success rates and the adaptation of step sizes in the (1+1)-evolution strategy. The results from the new adaptive scheme when applied to several test functions are compared with those obtained from the (1+1)-evolution strategy with a priori selected parameters. Our results indicate that assigning good reward measures seems to be crucial to the performance of the combined strategy.
{"title":"Step size adaptation in evolution strategies using reinforcement learning","authors":"Sibylle D. Müller, N. Schraudolph, P. Koumoutsakos","doi":"10.1109/CEC.2002.1006225","DOIUrl":"https://doi.org/10.1109/CEC.2002.1006225","url":null,"abstract":"We discuss the implementation of a learning algorithm for determining adaptation parameters in evolution strategies. As an initial test case, we consider the application of reinforcement learning for determining the relationship between success rates and the adaptation of step sizes in the (1+1)-evolution strategy. The results from the new adaptive scheme when applied to several test functions are compared with those obtained from the (1+1)-evolution strategy with a priori selected parameters. Our results indicate that assigning good reward measures seems to be crucial to the performance of the combined strategy.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114521236","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}
Pub Date : 2002-05-12DOI: 10.1109/CEC.2002.1004433
M. Wong, Shing Yan Lee, K. Leung
A novel hybrid framework is reported that improves upon our previous work, MDLEP, which uses evolutionary programming to solve the difficult Bayesian network learning problem. A new merge operator is also introduced that further enhances the efficiency. As experimental results suggest, our hybrid approach performs significantly better than MDLEP.
{"title":"A hybrid approach to learn Bayesian networks using evolutionary programming","authors":"M. Wong, Shing Yan Lee, K. Leung","doi":"10.1109/CEC.2002.1004433","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004433","url":null,"abstract":"A novel hybrid framework is reported that improves upon our previous work, MDLEP, which uses evolutionary programming to solve the difficult Bayesian network learning problem. A new merge operator is also introduced that further enhances the efficiency. As experimental results suggest, our hybrid approach performs significantly better than MDLEP.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"63 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121005529","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}
Pub Date : 2002-05-12DOI: 10.1109/CEC.2002.1007014
C. L. Valenzuela
A simple steady-state, Pareto-based evolutionary algorithm is presented that uses an elitist strategy for replacement and a simple uniform scheme for selection. Throughout the genetic search, progress depends entirely on the replacement policy, and no fitness calculations, rankings, subpopulations, niches or auxiliary populations are required. Preliminary results presented in this paper show improvements on previously published results for some multiple knapsack problems.
{"title":"A simple evolutionary algorithm for multi-objective optimization (SEAMO)","authors":"C. L. Valenzuela","doi":"10.1109/CEC.2002.1007014","DOIUrl":"https://doi.org/10.1109/CEC.2002.1007014","url":null,"abstract":"A simple steady-state, Pareto-based evolutionary algorithm is presented that uses an elitist strategy for replacement and a simple uniform scheme for selection. Throughout the genetic search, progress depends entirely on the replacement policy, and no fitness calculations, rankings, subpopulations, niches or auxiliary populations are required. Preliminary results presented in this paper show improvements on previously published results for some multiple knapsack problems.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116616778","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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