This paper describes a general scheme to convert sequential ant-based algorithms into parallel shared memory algorithms. The scheme is applied to an ant-based algorithm for the maximum clique problem. Extensive experimental results indicate that the parallel version provides noticeable improvements to the running time while maintaining comparable solution quality to that of the sequential version.
{"title":"Parallel shared memory strategies for ant-based optimization algorithms","authors":"T. N. Bui, ThanhVu Nguyen, Joseph R. Rizzo","doi":"10.1145/1569901.1569903","DOIUrl":"https://doi.org/10.1145/1569901.1569903","url":null,"abstract":"This paper describes a general scheme to convert sequential ant-based algorithms into parallel shared memory algorithms. The scheme is applied to an ant-based algorithm for the maximum clique problem. Extensive experimental results indicate that the parallel version provides noticeable improvements to the running time while maintaining comparable solution quality to that of the sequential version.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134599466","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}
Lourdes Araujo, J. J. M. Guervós, A. García, C. Cotta
In this paper we compare different policies to select individuals to migrate in an island model. Our thesis is that choosing individuals in a way that exploits differences between populations can enhance diversity, and improve the system performance. This has lead us to propose a family of policies that we call multikulti, in which nodes exchange individuals different "enough" among them. In this paper we present a policy according to which the receiver node chooses the most different individual among the sample received from the sending node. This sample is randomly built but only using individuals with a fitness above a threshold. This threshold is previously established by the receiving node. We have tested our system in two problems previously used in the evaluation of parallel systems, presenting different degree of difficulty. The multikulti policy presented herein has been proved to be more robust than other usual migration policies, such as sending the best or a random individual.
{"title":"Genotypic differences and migration policies in an island model","authors":"Lourdes Araujo, J. J. M. Guervós, A. García, C. Cotta","doi":"10.1145/1569901.1570080","DOIUrl":"https://doi.org/10.1145/1569901.1570080","url":null,"abstract":"In this paper we compare different policies to select individuals to migrate in an island model. Our thesis is that choosing individuals in a way that exploits differences between populations can enhance diversity, and improve the system performance. This has lead us to propose a family of policies that we call multikulti, in which nodes exchange individuals different \"enough\" among them. In this paper we present a policy according to which the receiver node chooses the most different individual among the sample received from the sending node. This sample is randomly built but only using individuals with a fitness above a threshold. This threshold is previously established by the receiving node. We have tested our system in two problems previously used in the evaluation of parallel systems, presenting different degree of difficulty. The multikulti policy presented herein has been proved to be more robust than other usual migration policies, such as sending the best or a random individual.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124266713","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}
Natural complex adaptive systems show many examples of self-organization and decentralization, such as pattern formation or swarm intelligence. Yet, only multicellular organisms possess the genuine architectural capabilities needed in many engineering application domains, from nanotechnologies to reconfigurable and swarm robotics. Biological development thus offers an important paradigm for a new breed of "evo-devo" computational systems. This work explores the evolutionary potential of an original multi-agent model of artificial embryogeny through differently parametrized simulations. It represents a rare attempt to integrate both self-organization and regulated architectures. Its aim is to illustrate how a developmental system, based on a truly indirect mapping from a modular genotype to a modular phenotype, can facilitate the generation of variations, thus structural innovation.
{"title":"Facilitating evolutionary innovation by developmental modularity and variability","authors":"R. Doursat","doi":"10.1145/1569901.1569996","DOIUrl":"https://doi.org/10.1145/1569901.1569996","url":null,"abstract":"Natural complex adaptive systems show many examples of self-organization and decentralization, such as pattern formation or swarm intelligence. Yet, only multicellular organisms possess the genuine architectural capabilities needed in many engineering application domains, from nanotechnologies to reconfigurable and swarm robotics. Biological development thus offers an important paradigm for a new breed of \"evo-devo\" computational systems. This work explores the evolutionary potential of an original multi-agent model of artificial embryogeny through differently parametrized simulations. It represents a rare attempt to integrate both self-organization and regulated architectures. Its aim is to illustrate how a developmental system, based on a truly indirect mapping from a modular genotype to a modular phenotype, can facilitate the generation of variations, thus structural innovation.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124483071","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}
Dynamic analyses of canonical particle swarm optimization (PSO) have indicated that parameter values of phi_max = 4.1 and constriction coefficient chi = 0.729 provide adequate exploration and prevent swarm explosion. This paper shows by example that these values do not prevent swarm explosion in some cases. In other examples it is shown that even when the swarm does not explode, the canonical PSO algorithm with these parameter values can still fail to converge indefinitely. A satisfactory analysis of PSO has yet to be made, and will require abandoning certain assumptions that oversimplify particle behavior.
{"title":"Particle swarm optimization in the presence of multiple global optima","authors":"Sunny Choi, B. Mayfield","doi":"10.1145/1569901.1570137","DOIUrl":"https://doi.org/10.1145/1569901.1570137","url":null,"abstract":"Dynamic analyses of canonical particle swarm optimization (PSO) have indicated that parameter values of phi_max = 4.1 and constriction coefficient chi = 0.729 provide adequate exploration and prevent swarm explosion. This paper shows by example that these values do not prevent swarm explosion in some cases. In other examples it is shown that even when the swarm does not explode, the canonical PSO algorithm with these parameter values can still fail to converge indefinitely. A satisfactory analysis of PSO has yet to be made, and will require abandoning certain assumptions that oversimplify particle behavior.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125594210","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}
Several practical problems in industry are difficult to optimize, both in terms of scalability and representation. Heuristics designed by domain experts are frequently applied to such problems. However, designing optimized heuristics can be a non-trivial task. One such difficult problem is the Facility Layout Problem (FLP) which is concerned with the allocation of activities to space. This paper is concerned with the block layout problem, where the activities require a fixed size and shape (modules). This problem is commonly divided into two sub problems; one of creating an initial feasible layout and one of improving the layout by interchanging the location of activities. We investigate how to extract novel heuristics for the FLP by applying an approach called Cooperative Coevolutionary Gene Expression Programming (CCGEP). By taking advantage of the natural problem decomposition, one species evolves heuristics for pre-scheduling, and another for allocating the activities onto the plant. An experimental, comparative approach investigates various features of the CCGEP approach. The results show that the evolved heuristics converge to suboptimal solutions as the problem size grows. However, coevolution has a positive effect on optimization of single problem instances. Expensive fitness evaluations may be limited by evolving generalized heuristics applicable to unseen fitness cases of arbitrary sizes.
{"title":"Scalability, generalization and coevolution -- experimental comparisons applied to automated facility layout planning","authors":"M. Furuholmen, K. Glette, M. Høvin, J. Tørresen","doi":"10.1145/1569901.1569997","DOIUrl":"https://doi.org/10.1145/1569901.1569997","url":null,"abstract":"Several practical problems in industry are difficult to optimize, both in terms of scalability and representation. Heuristics designed by domain experts are frequently applied to such problems. However, designing optimized heuristics can be a non-trivial task. One such difficult problem is the Facility Layout Problem (FLP) which is concerned with the allocation of activities to space. This paper is concerned with the block layout problem, where the activities require a fixed size and shape (modules). This problem is commonly divided into two sub problems; one of creating an initial feasible layout and one of improving the layout by interchanging the location of activities. We investigate how to extract novel heuristics for the FLP by applying an approach called Cooperative Coevolutionary Gene Expression Programming (CCGEP). By taking advantage of the natural problem decomposition, one species evolves heuristics for pre-scheduling, and another for allocating the activities onto the plant. An experimental, comparative approach investigates various features of the CCGEP approach. The results show that the evolved heuristics converge to suboptimal solutions as the problem size grows. However, coevolution has a positive effect on optimization of single problem instances. Expensive fitness evaluations may be limited by evolving generalized heuristics applicable to unseen fitness cases of arbitrary sizes.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115797390","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 characterization of fractured reservoirs involves: (1) the design of geological models integrating statistical and/or deterministic fracture properties; (2) the validation of flow simulation models by calibrating with dynamic field data e.g. well tests. The latter validation step is critical since it also validates the underlying geological model, it allows one to reduce some uncertainties among the fracture geometrical and distribution properties, and it is often the only mean to characterize fracture conductivities. However this is usually an ill-posed inverse problem: field data are usually not sufficient to fully characterize the fracture system. It is of interest to explore the parameters space effectively, so that multiple solutions may be characterized, and many production development scenarii may be studied. This paper presents a well tests inversion method to characterize fracture sets conductivities. The Covariance Matrix Adaptation-Evolution Strategy (CMA-ES) has been used as the optimization algorithm. It has been tested with some local optimization algorithms for comparison, and extended in order to detect several solutions simultaneously using a local proxy of the response surface. Moreover, uncertainty analyses are performed in regions of interest. Applications are presented for a fracture system with two fracture sets.
{"title":"An extended evolution strategy for the characterization of fracture conductivities from well tests","authors":"J. Bruyelle, A. Lange","doi":"10.1145/1569901.1570095","DOIUrl":"https://doi.org/10.1145/1569901.1570095","url":null,"abstract":"The characterization of fractured reservoirs involves: (1) the design of geological models integrating statistical and/or deterministic fracture properties; (2) the validation of flow simulation models by calibrating with dynamic field data e.g. well tests. The latter validation step is critical since it also validates the underlying geological model, it allows one to reduce some uncertainties among the fracture geometrical and distribution properties, and it is often the only mean to characterize fracture conductivities. However this is usually an ill-posed inverse problem: field data are usually not sufficient to fully characterize the fracture system. It is of interest to explore the parameters space effectively, so that multiple solutions may be characterized, and many production development scenarii may be studied. This paper presents a well tests inversion method to characterize fracture sets conductivities. The Covariance Matrix Adaptation-Evolution Strategy (CMA-ES) has been used as the optimization algorithm. It has been tested with some local optimization algorithms for comparison, and extended in order to detect several solutions simultaneously using a local proxy of the response surface. Moreover, uncertainty analyses are performed in regions of interest. Applications are presented for a fracture system with two fracture sets.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132994158","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 covariance matrix adaptation evolution strategy (CMAES) has proven to be a powerful method for reinforcement learning (RL). Recently, the CMA-ES has been augmented with an adaptive uncertainty handling mechanism. Because uncertainty is a typical property of RL problems this new algorithm, termed UH-CMA-ES, is promising for RL. The UH-CMA-ES dynamically adjusts the number of episodes considered in each evaluation of a policy. It controls the signal to noise ratio such that it is just high enough for a sufficiently good ranking of candidate policies, which in turn allows the evolutionary learning to find better solutions. This significantly increases the learning speed as well as the robustness without impairing the quality of the final solutions. We evaluate the UH-CMA-ES on fully and partially observable Markov decision processes with random start states and noisy observations. A canonical natural policy gradient method and random search serve as a baseline for comparison.
{"title":"Uncertainty handling CMA-ES for reinforcement learning","authors":"V. Heidrich-Meisner, C. Igel","doi":"10.1145/1569901.1570064","DOIUrl":"https://doi.org/10.1145/1569901.1570064","url":null,"abstract":"The covariance matrix adaptation evolution strategy (CMAES) has proven to be a powerful method for reinforcement learning (RL). Recently, the CMA-ES has been augmented with an adaptive uncertainty handling mechanism. Because uncertainty is a typical property of RL problems this new algorithm, termed UH-CMA-ES, is promising for RL. The UH-CMA-ES dynamically adjusts the number of episodes considered in each evaluation of a policy. It controls the signal to noise ratio such that it is just high enough for a sufficiently good ranking of candidate policies, which in turn allows the evolutionary learning to find better solutions. This significantly increases the learning speed as well as the robustness without impairing the quality of the final solutions. We evaluate the UH-CMA-ES on fully and partially observable Markov decision processes with random start states and noisy observations. A canonical natural policy gradient method and random search serve as a baseline for comparison.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132996748","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 recent years, program evolution algorithms based on the estimation of distribution algorithm (EDA) have been proposed to improve search ability of genetic programming (GP) and to overcome GP-hard problems. One such method is the probabilistic prototype tree (PPT) based algorithm. The PPT based method explores the optimal tree structure by using the full tree whose number of child nodes is maximum among possible trees. This algorithm, however, suffers from problems arising from function nodes having different number of child nodes. These function nodes cause intron nodes, which do not affect the fitness function. Moreover, the function nodes having many child nodes increase the search space and the number of samples necessary for properly constructing the probabilistic model. In order to solve this problem, we propose binary encoding for PPT. Here, we convert each function node to a subtree of binary nodes where the converted tree is correct in grammar. Our method reduces ineffectual search space, and the binary encoded tree is able to express the same tree structures as the original method. The effectiveness of the proposed method is demonstrated through the use of two computational experiments.
{"title":"Binary encoding for prototype tree of probabilistic model building GP","authors":"Toshihiko Yanase, Yoshihiko Hasegawa, H. Iba","doi":"10.1145/1569901.1570055","DOIUrl":"https://doi.org/10.1145/1569901.1570055","url":null,"abstract":"In recent years, program evolution algorithms based on the estimation of distribution algorithm (EDA) have been proposed to improve search ability of genetic programming (GP) and to overcome GP-hard problems. One such method is the probabilistic prototype tree (PPT) based algorithm. The PPT based method explores the optimal tree structure by using the full tree whose number of child nodes is maximum among possible trees. This algorithm, however, suffers from problems arising from function nodes having different number of child nodes. These function nodes cause intron nodes, which do not affect the fitness function. Moreover, the function nodes having many child nodes increase the search space and the number of samples necessary for properly constructing the probabilistic model. In order to solve this problem, we propose binary encoding for PPT. Here, we convert each function node to a subtree of binary nodes where the converted tree is correct in grammar. Our method reduces ineffectual search space, and the binary encoded tree is able to express the same tree structures as the original method. The effectiveness of the proposed method is demonstrated through the use of two computational experiments.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"206 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116125111","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}
Since the genomics revolution, bioinformatics has never been so popular. Many researchers have investigated with great success the use of evolutionary computation in bioinformatics [19] for example in the field of protein folding or determining genome sequences. In this paper, instead of using evolutionary computation as a way to provide new and innovative solutions to complex bioinformatics problems, we use genetic programming as a tool to evolve programs that are able to automatically classify research papers as dealing or not with a given protein. In a second part, we show that the attributes that are selected by the genetic programming evolved programs can be used efficiently for proteins classification.
{"title":"Genetic programming for protein related text classification","authors":"M. Segond, C. Fonlupt, D. Robilliard","doi":"10.1145/1569901.1570049","DOIUrl":"https://doi.org/10.1145/1569901.1570049","url":null,"abstract":"Since the genomics revolution, bioinformatics has never been so popular. Many researchers have investigated with great success the use of evolutionary computation in bioinformatics [19] for example in the field of protein folding or determining genome sequences. In this paper, instead of using evolutionary computation as a way to provide new and innovative solutions to complex bioinformatics problems, we use genetic programming as a tool to evolve programs that are able to automatically classify research papers as dealing or not with a given protein. In a second part, we show that the attributes that are selected by the genetic programming evolved programs can be used efficiently for proteins classification.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116712067","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}
{"title":"Session details: Track 9: genetic algorithms","authors":"Jano von Hemert, T. Lenaerts","doi":"10.1145/3257488","DOIUrl":"https://doi.org/10.1145/3257488","url":null,"abstract":"","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114769623","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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