Pub Date : 2003-12-08DOI: 10.1109/CEC.2003.1299611
M. Miki, T. Hiroyasu, Jun Wako, T. Yoshida
Simulated annealing (SA) is an effective general heuristic method for solving many combinatorial optimization problems. This paper deals with two problems in SA. One is the long computational time of the numerical annealings, and the solution to it is the parallel processing of SA. The other one is the determination of the appropriate temperature schedule in SA, and the solution to it is the introduction of an adaptive mechanism for changing the temperature. The multiple SA processes are performed in multiple processors, and the temperatures in the SA processes are determined by genetic algorithm. The proposed method is applied to solve many TSPs (travelling salesman problems) and JSPs (jobshop scheduling problems), and it is found that the method is very useful and effective.
{"title":"Adaptive temperature schedule determined by genetic algorithm for parallel simulated annealing","authors":"M. Miki, T. Hiroyasu, Jun Wako, T. Yoshida","doi":"10.1109/CEC.2003.1299611","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299611","url":null,"abstract":"Simulated annealing (SA) is an effective general heuristic method for solving many combinatorial optimization problems. This paper deals with two problems in SA. One is the long computational time of the numerical annealings, and the solution to it is the parallel processing of SA. The other one is the determination of the appropriate temperature schedule in SA, and the solution to it is the introduction of an adaptive mechanism for changing the temperature. The multiple SA processes are performed in multiple processors, and the temperatures in the SA processes are determined by genetic algorithm. The proposed method is applied to solve many TSPs (travelling salesman problems) and JSPs (jobshop scheduling problems), and it is found that the method is very useful and effective.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"69 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":"128429239","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.2003.1299924
F. Fernández, M. Tomassini, L. Vanneschi
A new technique for saving computing resources when using genetic programming is presented in this work. Instead of directly fighting bloat $the main factor explaining the large computational cost required for the evaluation of generations - by acting on individuals, we apply a new operator to the whole population: the plague. By removing some individuals every generation, we compensate for the increase in size of individuals, thus saving computing time when looking for solutions.
{"title":"Saving computational effort in genetic programming by means of plagues","authors":"F. Fernández, M. Tomassini, L. Vanneschi","doi":"10.1109/CEC.2003.1299924","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299924","url":null,"abstract":"A new technique for saving computing resources when using genetic programming is presented in this work. Instead of directly fighting bloat $the main factor explaining the large computational cost required for the evaluation of generations - by acting on individuals, we apply a new operator to the whole population: the plague. By removing some individuals every generation, we compensate for the increase in size of individuals, thus saving computing time when looking for solutions.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"44 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":"127497410","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.2003.1299906
G. Greenfield
We consider the problem of using evolutionary multiobjective optimization to evolve visual imagery. In our method, images (phenomes) are generated from expressions (genomes), and then color segmented so that they can be evaluated under a number of different aesthetic criteria. Our principal task is to formulate fitness functions that make the best use of these elementary aesthetic components. We demonstrate the benefits obtained from using more than one objective function. We also discuss technical issues that arose as a consequence of treating our computational aesthetics problem as a "real-world" application of evolutionary multiobjective optimization.
{"title":"Evolving aesthetic images using multiobjective optimization","authors":"G. Greenfield","doi":"10.1109/CEC.2003.1299906","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299906","url":null,"abstract":"We consider the problem of using evolutionary multiobjective optimization to evolve visual imagery. In our method, images (phenomes) are generated from expressions (genomes), and then color segmented so that they can be evaluated under a number of different aesthetic criteria. Our principal task is to formulate fitness functions that make the best use of these elementary aesthetic components. We demonstrate the benefits obtained from using more than one objective function. We also discuss technical issues that arose as a consequence of treating our computational aesthetics problem as a \"real-world\" application of evolutionary multiobjective optimization.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"8 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":"128895731","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.2003.1299844
B. Radha, R. King, H. Rughooputh
Network reconfiguration in distribution systems is realised by changing the status of sectionalizing switches and is usually done for loss reduction. The distribution reconfiguration belongs to a complex combinatorial optimization problem. This is because there are multiple constraints, which must not be violated while finding an optimal or near-optimal solution to the distribution network reconfiguration problem. An exhaustive search can definitely find the optimal solution but is computationally intensive. Moreover, solution produced by other heuristic search techniques often produce local optima. Consequently, to solve the problem with implementation simplicity, computation efficiency, solution feasibility and optimality, an improved method based on a modified genetic algorithm (GA) with real valued genes and an adaptive mutation rate is used. The distribution network reconfiguration (DNRC) model, in which the objective is to minimize the system power loss, is presented in this paper with application to 16-bus, 33-bus systems and a real distribution network of Mauritius.
{"title":"A modified genetic algorithm for optimal electrical distribution network reconfiguration","authors":"B. Radha, R. King, H. Rughooputh","doi":"10.1109/CEC.2003.1299844","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299844","url":null,"abstract":"Network reconfiguration in distribution systems is realised by changing the status of sectionalizing switches and is usually done for loss reduction. The distribution reconfiguration belongs to a complex combinatorial optimization problem. This is because there are multiple constraints, which must not be violated while finding an optimal or near-optimal solution to the distribution network reconfiguration problem. An exhaustive search can definitely find the optimal solution but is computationally intensive. Moreover, solution produced by other heuristic search techniques often produce local optima. Consequently, to solve the problem with implementation simplicity, computation efficiency, solution feasibility and optimality, an improved method based on a modified genetic algorithm (GA) with real valued genes and an adaptive mutation rate is used. The distribution network reconfiguration (DNRC) model, in which the objective is to minimize the system power loss, is presented in this paper with application to 16-bus, 33-bus systems and a real distribution network of Mauritius.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"84 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":"132411725","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.2003.1299642
Ming Chang, K. Ohkura, K. Ueda, M. Sugiyama
Multilevel selection theory views natural selection as hierarchy process that acts on any level of biological organizations whenever there exist heritable variation in fitness among units of that level. In this paper, selection schemes of evolutionary algorithms (EAs) are reconsidered from the point of view of the theory, and a novel constraint handling method is introduced in which a two-level selection process, namely within-group selection and between-group selection, is modeled to keep right balance between objective and penalty functions. The method is implemented on 3 group selection models that possessing different population structures and tested using (/spl mu/, /spl lambda/)-evolution strategies on a set of 13 benchmark problems. We show that a proper understanding of multilevel selection theory will help us to design EAs and might also enable us to challenge the old problems from a new angle.
{"title":"Group selection and its application to constrained evolutionary optimization","authors":"Ming Chang, K. Ohkura, K. Ueda, M. Sugiyama","doi":"10.1109/CEC.2003.1299642","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299642","url":null,"abstract":"Multilevel selection theory views natural selection as hierarchy process that acts on any level of biological organizations whenever there exist heritable variation in fitness among units of that level. In this paper, selection schemes of evolutionary algorithms (EAs) are reconsidered from the point of view of the theory, and a novel constraint handling method is introduced in which a two-level selection process, namely within-group selection and between-group selection, is modeled to keep right balance between objective and penalty functions. The method is implemented on 3 group selection models that possessing different population structures and tested using (/spl mu/, /spl lambda/)-evolution strategies on a set of 13 benchmark problems. We show that a proper understanding of multilevel selection theory will help us to design EAs and might also enable us to challenge the old problems from a new angle.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"192 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":"132862747","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.2003.1299614
C. Coello, M. Sierra
In this paper, we propose a first version of a multi-objective evolutionary algorithm that incorporates some coevolutionary concepts. The primary design goal of the proposed approach is to reduce the total number of objective function evaluations required to produce a reasonable good approximation of the true Pareto front of a problem. The main idea of the proposed approach is to concentrate the search effort on promising regions that arise during the evolutionary process as a byproduct of a mechanism that subdivides decision variable space based on an estimate of the relative importance of each decision variable. The proposed approach is validated using several test functions taken from the specialized literature and it is compared with respect to three approaches that are representative of the state-of-the-art in evolutionary multiobjective optimization.
{"title":"A coevolutionary multi-objective evolutionary algorithm","authors":"C. Coello, M. Sierra","doi":"10.1109/CEC.2003.1299614","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299614","url":null,"abstract":"In this paper, we propose a first version of a multi-objective evolutionary algorithm that incorporates some coevolutionary concepts. The primary design goal of the proposed approach is to reduce the total number of objective function evaluations required to produce a reasonable good approximation of the true Pareto front of a problem. The main idea of the proposed approach is to concentrate the search effort on promising regions that arise during the evolutionary process as a byproduct of a mechanism that subdivides decision variable space based on an estimate of the relative importance of each decision variable. The proposed approach is validated using several test functions taken from the specialized literature and it is compared with respect to three approaches that are representative of the state-of-the-art in evolutionary multiobjective optimization.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"61 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":"132094614","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.2003.1299387
Xiaohu Shi, Y. H. Lu, Chunguang Zhou, H. Lee, W. Z. Lin, Yanchun Liang
Inspired by the idea of genetic algorithm, we propose two hybrid evolutionary algorithms based on PSO and GA methods through crossing over the PSO and GA algorithms. The main ideas of the two proposed methods are to integrate PSO and GA methods in parallel and series forms respectively. Simulations for a series of benchmark test functions show that both of the two proposed methods possess better ability to find the global optimum than that of the standard PSO algorithm.
{"title":"Hybrid evolutionary algorithms based on PSO and GA","authors":"Xiaohu Shi, Y. H. Lu, Chunguang Zhou, H. Lee, W. Z. Lin, Yanchun Liang","doi":"10.1109/CEC.2003.1299387","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299387","url":null,"abstract":"Inspired by the idea of genetic algorithm, we propose two hybrid evolutionary algorithms based on PSO and GA methods through crossing over the PSO and GA algorithms. The main ideas of the two proposed methods are to integrate PSO and GA methods in parallel and series forms respectively. Simulations for a series of benchmark test functions show that both of the two proposed methods possess better ability to find the global optimum than that of the standard PSO algorithm.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"26 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":"132134854","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.2003.1299770
Chenn-Jung Huang, W. Lai
Many mechanisms based on bandwidth reservation have been proposed in the literature to decrease connection blocking probability and connection dropping probability in cellular communications. The handoff events occur at a much higher rate in sectored cellular networks than in traditional cellular systems. An efficient bandwidth reservation mechanism for the neighboring cells is therefore critical in the process of handoff during the connection of multimedia calls to avoid the unwillingly forced termination and waste of limited bandwidth in the sectored cellular communications, particularly when the handoff traffic is heavy. A self-adaptive bandwidth reservation scheme which employs a neural fuzzy bandwidth-reserving estimator, is proposed, and the simulation results show that our scheme can achieve superior performance than traditional fixed bandwidth-reserving scheme in sectored cellular networks when performance metrics are measured in terms of the new call blocking probability and the forced termination probability.
{"title":"Application of neuro-fuzzy technique to the bandwidth reservation for sectored cellular communications","authors":"Chenn-Jung Huang, W. Lai","doi":"10.1109/CEC.2003.1299770","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299770","url":null,"abstract":"Many mechanisms based on bandwidth reservation have been proposed in the literature to decrease connection blocking probability and connection dropping probability in cellular communications. The handoff events occur at a much higher rate in sectored cellular networks than in traditional cellular systems. An efficient bandwidth reservation mechanism for the neighboring cells is therefore critical in the process of handoff during the connection of multimedia calls to avoid the unwillingly forced termination and waste of limited bandwidth in the sectored cellular communications, particularly when the handoff traffic is heavy. A self-adaptive bandwidth reservation scheme which employs a neural fuzzy bandwidth-reserving estimator, is proposed, and the simulation results show that our scheme can achieve superior performance than traditional fixed bandwidth-reserving scheme in sectored cellular networks when performance metrics are measured in terms of the new call blocking probability and the forced termination probability.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"2 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":"130892530","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.2003.1299868
Wee-Chong Oon, Yew Jin Lim
This paper describes a series of experiments using co-evolution of artificial neural networks on a game called Kalah. The technique employed closely follows the one used by Chellapilla and Fogel to evolve the successful checkers program Anaconda. The experiments aim to provide insight on the effect of including piece differential information, a basic yet crucial piece of expert knowledge, into the neural network inputs.
{"title":"An investigation on piece differential information in co-evolution on games using Kalah","authors":"Wee-Chong Oon, Yew Jin Lim","doi":"10.1109/CEC.2003.1299868","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299868","url":null,"abstract":"This paper describes a series of experiments using co-evolution of artificial neural networks on a game called Kalah. The technique employed closely follows the one used by Chellapilla and Fogel to evolve the successful checkers program Anaconda. The experiments aim to provide insight on the effect of including piece differential information, a basic yet crucial piece of expert knowledge, into the neural network inputs.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"3 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":"129001152","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.2003.1299749
I. Sarafis, P. Trinder, A. Zalzala
We propose a new evolutionary algorithm for subspace clustering in very large and high-dimensional databases. The design includes task-specific coding and genetic operators, along with a nonrandom initialization procedure. Experimental results show that the algorithm scales almost linearly with the size and dimensionality of the database as well as the dimensionality of the hidden clusters. Our algorithm is able to discover clusters of different densities embedded in both low and high dimensional subspaces of the original space. Finally, the discovered knowledge is presented in the form of nonoverlapping clustering rules where only those features relevant to the clustering are reported. These two properties contributes to the relatively high comprehensibility of the clustering output.
{"title":"Towards effective subspace clustering with an evolutionary algorithm","authors":"I. Sarafis, P. Trinder, A. Zalzala","doi":"10.1109/CEC.2003.1299749","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299749","url":null,"abstract":"We propose a new evolutionary algorithm for subspace clustering in very large and high-dimensional databases. The design includes task-specific coding and genetic operators, along with a nonrandom initialization procedure. Experimental results show that the algorithm scales almost linearly with the size and dimensionality of the database as well as the dimensionality of the hidden clusters. Our algorithm is able to discover clusters of different densities embedded in both low and high dimensional subspaces of the original space. Finally, the discovered knowledge is presented in the form of nonoverlapping clustering rules where only those features relevant to the clustering are reported. These two properties contributes to the relatively high comprehensibility of the clustering output.","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":"128804416","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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