Pub Date : 2003-12-08DOI: 10.1109/CEC.2003.1299786
K. Tang, R. Jarvis
Research projects about evolution of agents in a cellular world are not new topics in the artificial life (AL) fields. However, most of the studies focus on those fundamental, social behaviours like energy preservation, pattern formation or leader following etc. This paper presents experiments about applications of genetic algorithms (GAs) to an empirical multiple robot cooperative task: unknown environment exploration. These experiments investigate the effectiveness of GAs for evolving behaviours of individual swarm members that constitute good collective results. They try to answer the questions of (i) Can GAs find such behaviours, or, do such behaviours exist? (ii) Are these behaviours sensitive to environmental changes?.
{"title":"Application of genetic algorithms to robotic swarm simulation","authors":"K. Tang, R. Jarvis","doi":"10.1109/CEC.2003.1299786","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299786","url":null,"abstract":"Research projects about evolution of agents in a cellular world are not new topics in the artificial life (AL) fields. However, most of the studies focus on those fundamental, social behaviours like energy preservation, pattern formation or leader following etc. This paper presents experiments about applications of genetic algorithms (GAs) to an empirical multiple robot cooperative task: unknown environment exploration. These experiments investigate the effectiveness of GAs for evolving behaviours of individual swarm members that constitute good collective results. They try to answer the questions of (i) Can GAs find such behaviours, or, do such behaviours exist? (ii) Are these behaviours sensitive to environmental changes?.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"370 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131582828","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 : 2003-12-08DOI: 10.1109/CEC.2003.1299815
Wouter Boomsma
A growing problem in the field of evolutionary computation is the large amount of genetic operators available for certain problem domains. This tendency is especially pronounced in areas where heuristics are used to create highly specialised operators. Even within the same problem domain, the performance of such operators often depends on the specific problem instance at hand. This results in a tedious and time-consuming process of comparing individual operator performances every time a new problem is to be solved. We investigate the use of adaptive operator scheduling to automate the operator selection process. The approach is tested on instances of the travelling salesman problem - a problem for which a long list of operators exists. Results show that benefits are twofold: Operator selection is achieved automatically and an overall performance improvement is observed.
{"title":"Using adaptive operator scheduling on problem domains with an operator manifold: applications to the travelling salesman problem","authors":"Wouter Boomsma","doi":"10.1109/CEC.2003.1299815","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299815","url":null,"abstract":"A growing problem in the field of evolutionary computation is the large amount of genetic operators available for certain problem domains. This tendency is especially pronounced in areas where heuristics are used to create highly specialised operators. Even within the same problem domain, the performance of such operators often depends on the specific problem instance at hand. This results in a tedious and time-consuming process of comparing individual operator performances every time a new problem is to be solved. We investigate the use of adaptive operator scheduling to automate the operator selection process. The approach is tested on instances of the travelling salesman problem - a problem for which a long list of operators exists. Results show that benefits are twofold: Operator selection is achieved automatically and an overall performance improvement is observed.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132262149","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 : 2003-12-08DOI: 10.1109/CEC.2003.1299644
J. Denzinger, Jordan Kidney
We present an improvement to distributed GAs based on migration of individuals between several concurrently evolving populations. The idea behind our improvement is to not only use the fitness of an individual as criterion for selecting the individuals that migrate, but also to consider the diversity of individuals versus the currently best individual. We experimentally show that a distributed GA using a weighted sum of fitness and a diversity measure for selecting migrating individuals finds the known optimal solutions to benchmark problems from literature (that offer a lot of local optima) on average substantially faster than the distributed GA using only fitness for selection. In addition, the run times of several runs of the distributed GA to the same problem instance vary much less with our improvement than in the base case, thus resulting in a more stable behavior of a distributed GA of this type.
{"title":"Improving migration by diversity","authors":"J. Denzinger, Jordan Kidney","doi":"10.1109/CEC.2003.1299644","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299644","url":null,"abstract":"We present an improvement to distributed GAs based on migration of individuals between several concurrently evolving populations. The idea behind our improvement is to not only use the fitness of an individual as criterion for selecting the individuals that migrate, but also to consider the diversity of individuals versus the currently best individual. We experimentally show that a distributed GA using a weighted sum of fitness and a diversity measure for selecting migrating individuals finds the known optimal solutions to benchmark problems from literature (that offer a lot of local optima) on average substantially faster than the distributed GA using only fitness for selection. In addition, the run times of several runs of the distributed GA to the same problem instance vary much less with our improvement than in the base case, thus resulting in a more stable behavior of a distributed GA of this type.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"268 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131924722","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 : 2003-12-08DOI: 10.1109/CEC.2003.1299428
J. Zydallis, G. Lamont
This research emphasizes explicit building block (BB) based MOEA performance with detailed symbolic representations. An explicit BB-based MOEA for solving constrained and real-world multiple objective problems (MOPs) is developed, the multiobjective messy genetic algorithm II (MOMGA-II) in order to validate symbolic BB concepts. This algorithm provides insight into solving difficult NP-complete MOPs that are generally not realized through the use of implicit BB-based MOEA approaches. Specific constrained integer problem examples include advanced logistics and modified knapsack problems. A primary focus is on generic repair mechanisms for generating feasible solutions per generation. The insight provided is necessary to increase the effectiveness and efficiency over all possible MOEA approaches.
{"title":"Explicit building-block multiobjective evolutionary algorithms for NPC problems","authors":"J. Zydallis, G. Lamont","doi":"10.1109/CEC.2003.1299428","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299428","url":null,"abstract":"This research emphasizes explicit building block (BB) based MOEA performance with detailed symbolic representations. An explicit BB-based MOEA for solving constrained and real-world multiple objective problems (MOPs) is developed, the multiobjective messy genetic algorithm II (MOMGA-II) in order to validate symbolic BB concepts. This algorithm provides insight into solving difficult NP-complete MOPs that are generally not realized through the use of implicit BB-based MOEA approaches. Specific constrained integer problem examples include advanced logistics and modified knapsack problems. A primary focus is on generic repair mechanisms for generating feasible solutions per generation. The insight provided is necessary to increase the effectiveness and efficiency over all possible MOEA approaches.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133493445","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 : 2003-12-08DOI: 10.1109/CEC.2003.1299928
Hussein A. Abbass
In this paper, we present a comparison between two multiobjective formulations to the formation of neuro-ensembles. The first formulation splits the training set into two nonoverlapping stratified subsets and form an objective to minimize the training error on each subset, while the second formulation adds random noise to the training set to form a second objective. A variation of the memetic Pareto artificial neural network (MPANN) algorithm is used. MPANN is based on differential evolution for continuous optimization. The ensemble is formed from all networks on the Pareto frontier. It is found that the first formulation outperformed the second. The first formulation is also found to be competitive to other methods in the literature.
{"title":"Pareto neuro-evolution: constructing ensemble of neural networks using multi-objective optimization","authors":"Hussein A. Abbass","doi":"10.1109/CEC.2003.1299928","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299928","url":null,"abstract":"In this paper, we present a comparison between two multiobjective formulations to the formation of neuro-ensembles. The first formulation splits the training set into two nonoverlapping stratified subsets and form an objective to minimize the training error on each subset, while the second formulation adds random noise to the training set to form a second objective. A variation of the memetic Pareto artificial neural network (MPANN) algorithm is used. MPANN is based on differential evolution for continuous optimization. The ensemble is formed from all networks on the Pareto frontier. It is found that the first formulation outperformed the second. The first formulation is also found to be competitive to other methods in the literature.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121984794","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 : 2003-12-08DOI: 10.1109/CEC.2003.1299925
Timothy G. W. Gordon
Traditional circuit design does not scale well to large, complex problems. Nature solves the scalability problem by using a complex mapping implicit in the process of biological development. By modelling this process we aim to improve scalability in evolutionary circuit design. Here we extend our earlier work (Gordon and Bentley, 2002) by demonstrating that evolution can learn and encode useful circuit design abstractions in a developmental process. We go on to present enhanced models of development with improved intercellular communication and show how this improves their ability to generate circuits.
{"title":"Exploring models of development for evolutionary circuit design","authors":"Timothy G. W. Gordon","doi":"10.1109/CEC.2003.1299925","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299925","url":null,"abstract":"Traditional circuit design does not scale well to large, complex problems. Nature solves the scalability problem by using a complex mapping implicit in the process of biological development. By modelling this process we aim to improve scalability in evolutionary circuit design. Here we extend our earlier work (Gordon and Bentley, 2002) by demonstrating that evolution can learn and encode useful circuit design abstractions in a developmental process. We go on to present enhanced models of development with improved intercellular communication and show how this improves their ability to generate circuits.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"257 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115420528","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 : 2003-12-08DOI: 10.1109/CEC.2003.1299903
Maumita Bhattacharya, Guojun Lu
A dynamic approximate fitness-based hybrid evolutionary algorithm is presented here. The proposed model partially replaces expensive fitness evaluation by an approximate model. A cluster-based intelligent guided technique is used to decide on use of expensive function evaluation and dynamically adapt the predicted model. Avoiding expensive function evaluation speeds of the optimisation process. Also additional information derived from the predicted model at lower computational expense, is exploited to improve solution. Experimental findings support the theoretical basis of the proposed framework.
{"title":"DAFHEA: a dynamic approximate fitness-based hybrid EA for optimisation problems","authors":"Maumita Bhattacharya, Guojun Lu","doi":"10.1109/CEC.2003.1299903","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299903","url":null,"abstract":"A dynamic approximate fitness-based hybrid evolutionary algorithm is presented here. The proposed model partially replaces expensive fitness evaluation by an approximate model. A cluster-based intelligent guided technique is used to decide on use of expensive function evaluation and dynamically adapt the predicted model. Avoiding expensive function evaluation speeds of the optimisation process. Also additional information derived from the predicted model at lower computational expense, is exploited to improve solution. Experimental findings support the theoretical basis of the proposed framework.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115528726","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 : 2003-12-08DOI: 10.1109/CEC.2003.1299620
Hoi Shun Miu, K. Leung, Yee Leung
This paper proposes a new approach to combine the knowledge-based model and the cooperation technique of evolutionary agents to identify the location of the desired object in a satellite image. The agents interact with the local information of the image pixels to search for the target objects through an evolutionary process. A new set of fitness function and evolutionary operators are defined for the process. The decentralized, bottom-up and evolutionary natures of the agents can be used to construct a robust system for object recognition in satellite images. The experimental results are satisfactory and have demonstrated the flexibility and power of the approach.
{"title":"An evolutionary multi-agent system for object recognition in satellite images","authors":"Hoi Shun Miu, K. Leung, Yee Leung","doi":"10.1109/CEC.2003.1299620","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299620","url":null,"abstract":"This paper proposes a new approach to combine the knowledge-based model and the cooperation technique of evolutionary agents to identify the location of the desired object in a satellite image. The agents interact with the local information of the image pixels to search for the target objects through an evolutionary process. A new set of fitness function and evolutionary operators are defined for the process. The decentralized, bottom-up and evolutionary natures of the agents can be used to construct a robust system for object recognition in satellite images. The experimental results are satisfactory and have demonstrated the flexibility and power of the approach.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116920499","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 : 2003-12-08DOI: 10.1109/CEC.2003.1299831
S. Aupetit, V. Bordeau, N. Monmarché, M. Slimane, G. Venturini
We present how we use an interactive genetic algorithm to find the best parameters to build an artificial art work according to user's aesthetic taste. Ants are used to spread colors on a numerical painting and behave with very simple rules to follow and deposit colors. These rules and colors are considered as parameters for the evolutionary process. This work can be considered as a contribution to naturally inspired artificial art and evolutionary techniques are used to help artists in their creative process.
{"title":"Interactive evolution of ant paintings","authors":"S. Aupetit, V. Bordeau, N. Monmarché, M. Slimane, G. Venturini","doi":"10.1109/CEC.2003.1299831","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299831","url":null,"abstract":"We present how we use an interactive genetic algorithm to find the best parameters to build an artificial art work according to user's aesthetic taste. Ants are used to spread colors on a numerical painting and behave with very simple rules to follow and deposit colors. These rules and colors are considered as parameters for the evolutionary process. This work can be considered as a contribution to naturally inspired artificial art and evolutionary techniques are used to help artists in their creative process.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"202 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115011576","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 : 2003-12-08DOI: 10.1109/CEC.2003.1299640
Shengyin Wang, K. Tai
A bit-array representation method for structural topology optimization using the GA is proposed. The importance of design connectivity is further emphasized and a hierarchical violation penalty method is proposed to penalize the violated constraint functions so that the problem of representation degeneracy can be overcome and the GA search can be driven towards the combination of better structural performance, less unusable material and fewer connected objects in the design domain. An identical initialization method is also proposed to test the performance of the GA operators. With the appropriately selected GA operators, the bit-array representation GA is applied to the structural topology optimization problems of minimum weight. Numerical results demonstrate that the present GA can achieve better accuracy with less computational cost and suggest that the GA performance can be significantly improved by handling the design connectivity properly.
{"title":"A bit-array representation GA for structural topology optimization","authors":"Shengyin Wang, K. Tai","doi":"10.1109/CEC.2003.1299640","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299640","url":null,"abstract":"A bit-array representation method for structural topology optimization using the GA is proposed. The importance of design connectivity is further emphasized and a hierarchical violation penalty method is proposed to penalize the violated constraint functions so that the problem of representation degeneracy can be overcome and the GA search can be driven towards the combination of better structural performance, less unusable material and fewer connected objects in the design domain. An identical initialization method is also proposed to test the performance of the GA operators. With the appropriately selected GA operators, the bit-array representation GA is applied to the structural topology optimization problems of minimum weight. Numerical results demonstrate that the present GA can achieve better accuracy with less computational cost and suggest that the GA performance can be significantly improved by handling the design connectivity properly.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116723646","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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