Jorng-Tzong Horng, Li-Yi Lin, Baw-Jhiune Liu, Cheng-Yan Kao
This investigation presents an adapted genetic algorithm to resolve simple plant location problems. The proposed algorithm applies a clustering technique as mutation guidance and a novel local search method to enhance the solution quality. The proposed algorithm is then applied to the fifteen test problems taken from Beasley's OR-Library (J.E. Beasley, 1990). Empirical results indicate that the error rate of the proposed adapted GA is less than 0.3 percent. In addition, the computational time is bounded by a polynomial function of the problem size.
{"title":"Resolution of simple plant location problems using an adapted genetic algorithm","authors":"Jorng-Tzong Horng, Li-Yi Lin, Baw-Jhiune Liu, Cheng-Yan Kao","doi":"10.1109/CEC.1999.782570","DOIUrl":"https://doi.org/10.1109/CEC.1999.782570","url":null,"abstract":"This investigation presents an adapted genetic algorithm to resolve simple plant location problems. The proposed algorithm applies a clustering technique as mutation guidance and a novel local search method to enhance the solution quality. The proposed algorithm is then applied to the fifteen test problems taken from Beasley's OR-Library (J.E. Beasley, 1990). Empirical results indicate that the error rate of the proposed adapted GA is less than 0.3 percent. In addition, the computational time is bounded by a polynomial function of the problem size.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121585090","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}
Financial markets data present a challenging opportunity for the learning of complex patterns not otherwise discernable, and machine learning techniques like genetic algorithms have been noted to be advantageous in this regard. Independent trials of the genetic algorithm are known to explore different parts of the search space and produce solutions which potentially capture different patterns in the data. Additionally, learning in domains prone to noisy data can generate solutions which obtain performance gains by fitting to what essentially is noise in the data. The article investigates possible strategies for combining the rules obtained from independent GA trials with the objective of noise filtering or enhanced pattern detection for improving the overall learning accuracy.
{"title":"Combining rules learnt using genetic algorithms for financial forecasting","authors":"K. Mehta, S. Bhattacharyya","doi":"10.1109/CEC.1999.782581","DOIUrl":"https://doi.org/10.1109/CEC.1999.782581","url":null,"abstract":"Financial markets data present a challenging opportunity for the learning of complex patterns not otherwise discernable, and machine learning techniques like genetic algorithms have been noted to be advantageous in this regard. Independent trials of the genetic algorithm are known to explore different parts of the search space and produce solutions which potentially capture different patterns in the data. Additionally, learning in domains prone to noisy data can generate solutions which obtain performance gains by fitting to what essentially is noise in the data. The article investigates possible strategies for combining the rules obtained from independent GA trials with the objective of noise filtering or enhanced pattern detection for improving the overall learning accuracy.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114727223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We analyze the fitness dynamics of a (1+1) mutation-only genetic algorithm (GA) operating on a family of simple time-dependent fitness functions. Resulting models of behavior are used in the prediction of GA performance on this fitness function. The accuracy of performance predictions are compared to actual GA runs, and results are discussed in relation to analyses of the stationary version of the dynamic fitness landscape and to prior work performed in the field of evolutionary optimization of dynamic fitness functions.
{"title":"(1+1) genetic algorithm fitness dynamics in a changing environment","authors":"S. Stanhope, J. Daida","doi":"10.1109/CEC.1999.785499","DOIUrl":"https://doi.org/10.1109/CEC.1999.785499","url":null,"abstract":"We analyze the fitness dynamics of a (1+1) mutation-only genetic algorithm (GA) operating on a family of simple time-dependent fitness functions. Resulting models of behavior are used in the prediction of GA performance on this fitness function. The accuracy of performance predictions are compared to actual GA runs, and results are discussed in relation to analyses of the stationary version of the dynamic fitness landscape and to prior work performed in the field of evolutionary optimization of dynamic fitness functions.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"224 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114872507","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 combination of evolutionary algorithms and neural networks for the purpose of structure optimization has frequently been discussed. In this paper we apply an indirect encoding method, the recursive encoding combined with a gradual growth process of the network structure, to the problem of time series prediction and modelling. Modularity of the network structure, the optimization of the encoding parameters on a larger time-scale, i.e., a meta-evolutionary process and the choice of encoding dependent search operators to enhance the strong causality of the search process are discussed.
{"title":"Variable encoding of modular neural networks for time series prediction","authors":"B. Sendhoff, M. Kreutz","doi":"10.1109/CEC.1999.781934","DOIUrl":"https://doi.org/10.1109/CEC.1999.781934","url":null,"abstract":"The combination of evolutionary algorithms and neural networks for the purpose of structure optimization has frequently been discussed. In this paper we apply an indirect encoding method, the recursive encoding combined with a gradual growth process of the network structure, to the problem of time series prediction and modelling. Modularity of the network structure, the optimization of the encoding parameters on a larger time-scale, i.e., a meta-evolutionary process and the choice of encoding dependent search operators to enhance the strong causality of the search process are discussed.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127890063","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 paper deals with a fundamental problem arising in the design of optimal networks-the maximization of the number of spanning trees. To make the problem computationally tractable, we consider a class of regular graphs. The problem is solved with the use of the evolutionary algorithm and compared to the 2-opt method. The problem-specific genetic operators are introduced, Various experiments with different graph structures have been performed, the results are reported and discussed. The influence of introducing some preliminary knowledge about the problem on the algorithm effectiveness is studied.
{"title":"Designing regular graphs with the use of evolutionary algorithms","authors":"B. Sawionek, J. Wojciechowski, J. Arabas","doi":"10.1109/CEC.1999.785497","DOIUrl":"https://doi.org/10.1109/CEC.1999.785497","url":null,"abstract":"The paper deals with a fundamental problem arising in the design of optimal networks-the maximization of the number of spanning trees. To make the problem computationally tractable, we consider a class of regular graphs. The problem is solved with the use of the evolutionary algorithm and compared to the 2-opt method. The problem-specific genetic operators are introduced, Various experiments with different graph structures have been performed, the results are reported and discussed. The influence of introducing some preliminary knowledge about the problem on the algorithm effectiveness is studied.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115846956","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 purpose of the paper is to analyze the effects of bounded rationality and the mimicry strategy in designing reliable networks in the domain of the social dilemma. There is growing literature on bounded rationality and the evolutional approach. The hypotheses employed in this research reflect the limited ability of each player or agent to receive, decide, and act upon information they get in the course of interactions. Our model can be interpreted in a like manner, however, we intend to combine the evolutional approach and the concept of bounded rationality. We consider the situation where a group of agents is repeatedly matched to play a game. Each agent only interacts with his neighbors, and when agents react, they react myopically (the myopia hypothesis). Agents are completely naive and do not perform optimization calculations. Rather, agents sometimes observe the current performance of other agents, and simply mimic the most successful strategy.
{"title":"An evolutionary design of the networks of mutual reliability","authors":"K. Uno, A. Namatame","doi":"10.1109/CEC.1999.785481","DOIUrl":"https://doi.org/10.1109/CEC.1999.785481","url":null,"abstract":"The purpose of the paper is to analyze the effects of bounded rationality and the mimicry strategy in designing reliable networks in the domain of the social dilemma. There is growing literature on bounded rationality and the evolutional approach. The hypotheses employed in this research reflect the limited ability of each player or agent to receive, decide, and act upon information they get in the course of interactions. Our model can be interpreted in a like manner, however, we intend to combine the evolutional approach and the concept of bounded rationality. We consider the situation where a group of agents is repeatedly matched to play a game. Each agent only interacts with his neighbors, and when agents react, they react myopically (the myopia hypothesis). Agents are completely naive and do not perform optimization calculations. Rather, agents sometimes observe the current performance of other agents, and simply mimic the most successful strategy.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131979333","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}
Given a representation in an evolutionary computation method, there are a number of variation operators that can be applied to extant solutions in the population to create new solutions. These variation operators can generally be classified into two broad categories, exploratory and exploitative operators. While exploratory operators allow for the traversal of a given search space, exploitative operators induce behavior that causes the solution to move towards nearby locally optimal points on the fitness landscape. Fitness distribution analysis is a recent technique for assessing the reliability and quality of variation operators in light of an objective function to be optimized. This technique is applied to the evolution of modular and non-modular finite state machines. Experiments are conducted on two instances of a tracking problem. Discussion is directed towards assessing the overall effectiveness of operators for such machines. The effect of the employed operators is consistent with previous intuitions when non-modular FSMs are used. Experiments using modular FSMs indicate a more exploratory nature for the employed variation operators. Results indicate a high degree of sensitivity to the employed variation operators when applied to modular FSMs.
{"title":"Assessing operator effectiveness on finite state machines using fitness distributions","authors":"David Czarnecki","doi":"10.1109/CEC.1999.785504","DOIUrl":"https://doi.org/10.1109/CEC.1999.785504","url":null,"abstract":"Given a representation in an evolutionary computation method, there are a number of variation operators that can be applied to extant solutions in the population to create new solutions. These variation operators can generally be classified into two broad categories, exploratory and exploitative operators. While exploratory operators allow for the traversal of a given search space, exploitative operators induce behavior that causes the solution to move towards nearby locally optimal points on the fitness landscape. Fitness distribution analysis is a recent technique for assessing the reliability and quality of variation operators in light of an objective function to be optimized. This technique is applied to the evolution of modular and non-modular finite state machines. Experiments are conducted on two instances of a tracking problem. Discussion is directed towards assessing the overall effectiveness of operators for such machines. The effect of the employed operators is consistent with previous intuitions when non-modular FSMs are used. Experiments using modular FSMs indicate a more exploratory nature for the employed variation operators. Results indicate a high degree of sensitivity to the employed variation operators when applied to modular FSMs.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132039086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A genetic programming paradigm using Lindenmayer system re-writing grammars is proposed as a means of specifying robot behaviors for autonomous navigation of mobile robots in uncertain environments. The concise nature of these algorithms and their inherent expansion capabilities hold promise as a method of overcoming communication bandwidth and time-of-flight limitations in the transmission of navigation, guidance, and control algorithms of planetary rovers. Though preliminary, the results of this early research show much promise as a viable programming technique for evolutionary robotics and embedded systems.
{"title":"Morphogenesis of path plan sequences through genetic synthesis of L-system productions","authors":"C. Schaefer","doi":"10.1109/CEC.1999.781947","DOIUrl":"https://doi.org/10.1109/CEC.1999.781947","url":null,"abstract":"A genetic programming paradigm using Lindenmayer system re-writing grammars is proposed as a means of specifying robot behaviors for autonomous navigation of mobile robots in uncertain environments. The concise nature of these algorithms and their inherent expansion capabilities hold promise as a method of overcoming communication bandwidth and time-of-flight limitations in the transmission of navigation, guidance, and control algorithms of planetary rovers. Though preliminary, the results of this early research show much promise as a viable programming technique for evolutionary robotics and embedded systems.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132323077","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}
Genetic programming has rarely been applied to manufacturing optimisation problems. In this report we investigate the potential use of genetic programming for the solution of the one-machine total tardiness problem. Combinations of dispatching rules are employed as an indirect way of representing permutations within a modified genetic programming framework. Hybridisation of genetic programming with local search techniques is also introduced, in an attempt to improve the quality of solutions. All the algorithms are tested on a large number of benchmark problems with different levels of tardiness and tightness of due dates.
{"title":"A genetic programming heuristic for the one-machine total tardiness problem","authors":"C. Dimopoulos, A. Zalzala","doi":"10.1109/CEC.1999.785549","DOIUrl":"https://doi.org/10.1109/CEC.1999.785549","url":null,"abstract":"Genetic programming has rarely been applied to manufacturing optimisation problems. In this report we investigate the potential use of genetic programming for the solution of the one-machine total tardiness problem. Combinations of dispatching rules are employed as an indirect way of representing permutations within a modified genetic programming framework. Hybridisation of genetic programming with local search techniques is also introduced, in an attempt to improve the quality of solutions. All the algorithms are tested on a large number of benchmark problems with different levels of tardiness and tightness of due dates.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134537116","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}
Zhenya He, Chengjian Wei, Bingyao Jin, Wenjiang Pei, Luxi Yang
In this paper binary population-based incremental learning is extended to an integer form, and a new approach to the traveling salesman problem (TSP) is proposed based on linkage relations between cities. The properties of this method are distributed stochastic tour construction, probability distribution initialization, accelerated search based on some power of probability, decision of entropy of probability distribution for terminate condition on process of evolution, and improvement of population solutions using 2-opt/3-opt. Thirteen TSP problems are solved, including ten international contest problems on symmetric and asymmetric TSP problems. The results show that the method proposed in this paper is comparable to the international advanced level on TSP problems and is capable of finding high quality solutions to TSP problems in short times, particularly for large TSP instances.
{"title":"A new population-based incremental learning method for the traveling salesman problem","authors":"Zhenya He, Chengjian Wei, Bingyao Jin, Wenjiang Pei, Luxi Yang","doi":"10.1109/CEC.1999.782553","DOIUrl":"https://doi.org/10.1109/CEC.1999.782553","url":null,"abstract":"In this paper binary population-based incremental learning is extended to an integer form, and a new approach to the traveling salesman problem (TSP) is proposed based on linkage relations between cities. The properties of this method are distributed stochastic tour construction, probability distribution initialization, accelerated search based on some power of probability, decision of entropy of probability distribution for terminate condition on process of evolution, and improvement of population solutions using 2-opt/3-opt. Thirteen TSP problems are solved, including ten international contest problems on symmetric and asymmetric TSP problems. The results show that the method proposed in this paper is comparable to the international advanced level on TSP problems and is capable of finding high quality solutions to TSP problems in short times, particularly for large TSP instances.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131831102","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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