Pub Date : 2003-12-08DOI: 10.1109/CEC.2003.1299784
Wenjun Yin, Min Liu, Cheng Wu
Genetic programming (GP) has been rarely applied to scheduling problems. In this paper the use of GP to learn single-machine predictive scheduling (PS) heuristics with stochastic breakdowns is investigated, where both tardiness and stability objectives in face of machine failures are considered. The proposed bi-tree structured representation scheme makes it possible to search sequencing and idle time inserting programs integratedly. Empirical results in different uncertain environments show that GP can evolve high quality PS heuristics effectively. The roles of inserted idle time are then analysed with respect to various weighting objectives. Finally some guides are supplied for PS design based on GP-evolved heuristics.
{"title":"Learning single-machine scheduling heuristics subject to machine breakdowns with genetic programming","authors":"Wenjun Yin, Min Liu, Cheng Wu","doi":"10.1109/CEC.2003.1299784","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299784","url":null,"abstract":"Genetic programming (GP) has been rarely applied to scheduling problems. In this paper the use of GP to learn single-machine predictive scheduling (PS) heuristics with stochastic breakdowns is investigated, where both tardiness and stability objectives in face of machine failures are considered. The proposed bi-tree structured representation scheme makes it possible to search sequencing and idle time inserting programs integratedly. Empirical results in different uncertain environments show that GP can evolve high quality PS heuristics effectively. The roles of inserted idle time are then analysed with respect to various weighting objectives. Finally some guides are supplied for PS design based on GP-evolved heuristics.","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":"133487564","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.2003.1299637
T. Okabe, K. Foli, M. Olhofer, Yaochu Jin, B. Sendhoff
Although many methods for dealing with multi-objective optimisation (MOO) problems are available as stated in K. Deb (2001) and successful applications have been reported on C.A. Coello et al. (2001), the comparison between MOO methods applied to real-world problem was rarely carried out. This paper reports the comparison between MOO methods applied to a real-world problem, namely, the optimization of a micro heat exchanger (/spl mu/HEX). Two MOO methods, dynamically weighted aggregation (DWA) proposed by Y. Jin et al. (2001) and non-dominated sorting genetic algorithms (NSGA-II) proposed by K. Deb et al. (2000) and K. Deb et al. (2002), were used for the study. The commercial computational fluid dynamics (CFD) solver CFD-ACE+ is used to evaluate fitness. We introduce how to interface the commercial solver with evolutionary computation (EC) and also report the necessary functionalities of the commercial solver to be used for the optimisation.
{"title":"Comparative studies on micro heat exchanger optimisation","authors":"T. Okabe, K. Foli, M. Olhofer, Yaochu Jin, B. Sendhoff","doi":"10.1109/CEC.2003.1299637","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299637","url":null,"abstract":"Although many methods for dealing with multi-objective optimisation (MOO) problems are available as stated in K. Deb (2001) and successful applications have been reported on C.A. Coello et al. (2001), the comparison between MOO methods applied to real-world problem was rarely carried out. This paper reports the comparison between MOO methods applied to a real-world problem, namely, the optimization of a micro heat exchanger (/spl mu/HEX). Two MOO methods, dynamically weighted aggregation (DWA) proposed by Y. Jin et al. (2001) and non-dominated sorting genetic algorithms (NSGA-II) proposed by K. Deb et al. (2000) and K. Deb et al. (2002), were used for the study. The commercial computational fluid dynamics (CFD) solver CFD-ACE+ is used to evaluate fitness. We introduce how to interface the commercial solver with evolutionary computation (EC) and also report the necessary functionalities of the commercial solver to be used for the optimisation.","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":"115713025","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.2003.1299577
D. V. D. Merwe, A. Engelbrecht
This paper proposes two new approaches to using PSO to cluster data. It is shown how PSO can be used to find the centroids of a user specified number of clusters. The algorithm is then extended to use K-means clustering to seed the initial swarm. This second algorithm basically uses PSO to refine the clusters formed by K-means. The new PSO algorithms are evaluated on six data sets, and compared to the performance of K-means clustering. Results show that both PSO clustering techniques have much potential.
{"title":"Data clustering using particle swarm optimization","authors":"D. V. D. Merwe, A. Engelbrecht","doi":"10.1109/CEC.2003.1299577","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299577","url":null,"abstract":"This paper proposes two new approaches to using PSO to cluster data. It is shown how PSO can be used to find the centroids of a user specified number of clusters. The algorithm is then extended to use K-means clustering to seed the initial swarm. This second algorithm basically uses PSO to refine the clusters formed by K-means. The new PSO algorithms are evaluated on six data sets, and compared to the performance of K-means clustering. Results show that both PSO clustering techniques have much potential.","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":"124560704","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.2003.1299874
J. Jeon, K. Yoo
This study presents a Montgomery multiplication architecture using irreducible all one polynomial (AOP) in GF(2/sup m/) based on programmable cellular automata (PCA). The proposed architecture has the advantage of high regularity and a reduced latency based on combining the characteristics of irreducible AOP and PCA. The proposed architecture is possible to implement the modular exponentiation, division, inversion architectures.
{"title":"Design of Montgomery multiplication architecture based on programmable cellular automata","authors":"J. Jeon, K. Yoo","doi":"10.1109/CEC.2003.1299874","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299874","url":null,"abstract":"This study presents a Montgomery multiplication architecture using irreducible all one polynomial (AOP) in GF(2/sup m/) based on programmable cellular automata (PCA). The proposed architecture has the advantage of high regularity and a reduced latency based on combining the characteristics of irreducible AOP and PCA. The proposed architecture is possible to implement the modular exponentiation, division, inversion architectures.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"29 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":"114309488","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.2003.1299615
Joseph A. Rothermich, Fang Wang, J. Miller
A cell based optimization (CBO) algorithm is proposed which takes inspiration from the collective behaviour of cellular slime molds (Dictyostellium discoideum). Experiments with CBO are conducted to study the ability of simple cell-like agents to collectively manage resources across a distributed network. Cells, or agents, only have local information can signal, move, divide, and die. Heterogeneous populations of the cells are evolved using Cartesian genetic programming (CGP). Several experiments were carried out to examine the adaptation of cells to changing user demand patterns. CBO performance was compared using various methods to change demand. The experiments showed that populations consistently evolve to produce effective solutions. The populations produce better solutions when user demand patterns fluctuated over time instead of environments with static demand. This is a surprising result that shows that populations need to be challenged during the evolutionary process to produce good results.
{"title":"Adaptivity in cell based optimization for information ecosystems","authors":"Joseph A. Rothermich, Fang Wang, J. Miller","doi":"10.1109/CEC.2003.1299615","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299615","url":null,"abstract":"A cell based optimization (CBO) algorithm is proposed which takes inspiration from the collective behaviour of cellular slime molds (Dictyostellium discoideum). Experiments with CBO are conducted to study the ability of simple cell-like agents to collectively manage resources across a distributed network. Cells, or agents, only have local information can signal, move, divide, and die. Heterogeneous populations of the cells are evolved using Cartesian genetic programming (CGP). Several experiments were carried out to examine the adaptation of cells to changing user demand patterns. CBO performance was compared using various methods to change demand. The experiments showed that populations consistently evolve to produce effective solutions. The populations produce better solutions when user demand patterns fluctuated over time instead of environments with static demand. This is a surprising result that shows that populations need to be challenged during the evolutionary process to produce good results.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"55 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":"116360274","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.2003.1299415
B. Liu, Bob McKay, H. Abbass
We present a boosting genetic algorithm for classification rule discovery. The method is based on the iterative rule learning approach to genetic classifiers. The boosting mechanism increases the weight of those training instances that are not classified correctly by the new rules, so that in the next iteration the algorithm focuses the search on those rules that capture the misclassified or uncovered instances. We show that the boosted genetic classifier has higher accuracy for prediction, or from an alternative and perhaps more important perspective, uses less computational resources for similar accuracy, than the original genetic classifier.
{"title":"Improving genetic classifiers with a boosting algorithm","authors":"B. Liu, Bob McKay, H. Abbass","doi":"10.1109/CEC.2003.1299415","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299415","url":null,"abstract":"We present a boosting genetic algorithm for classification rule discovery. The method is based on the iterative rule learning approach to genetic classifiers. The boosting mechanism increases the weight of those training instances that are not classified correctly by the new rules, so that in the next iteration the algorithm focuses the search on those rules that capture the misclassified or uncovered instances. We show that the boosted genetic classifier has higher accuracy for prediction, or from an alternative and perhaps more important perspective, uses less computational resources for similar accuracy, than the original genetic classifier.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"356 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":"123664600","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.2003.1299839
O. Kittithreerapronchai, Charles Anderson
We studied the dynamic allocation of trucks to paint booths, contrasting two previously proposed schemes in which booths bid against each other for trucks: one based on markets and the other ant-inspired response thresholds. We explore parameter space for several system performance metrics and find that this system is surprisingly easy to optimize and that a number of parameters can be eliminated. We investigate two different threshold reinforcement schemes that give rise to booth specialization and also examine variations of the breaking tie rules that decide among booths when two or more place identical, highest bids for a particular truck. We find that the threshold reinforcement scheme usually used in response threshold applications (local update) fares worse than one with global update of thresholds, and that breaking tie rules previously proposed can be simplified without loss of system performance.
{"title":"Do ants paint trucks better than chickens? Markets versus response thresholds for distributed dynamic scheduling","authors":"O. Kittithreerapronchai, Charles Anderson","doi":"10.1109/CEC.2003.1299839","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299839","url":null,"abstract":"We studied the dynamic allocation of trucks to paint booths, contrasting two previously proposed schemes in which booths bid against each other for trucks: one based on markets and the other ant-inspired response thresholds. We explore parameter space for several system performance metrics and find that this system is surprisingly easy to optimize and that a number of parameters can be eliminated. We investigate two different threshold reinforcement schemes that give rise to booth specialization and also examine variations of the breaking tie rules that decide among booths when two or more place identical, highest bids for a particular truck. We find that the threshold reinforcement scheme usually used in response threshold applications (local update) fares worse than one with global update of thresholds, and that breaking tie rules previously proposed can be simplified without loss of system performance.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"82 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":"117149341","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.2003.1299592
C. Congdon, Kevin J. Septor
Gaphyl is an application of evolutionary algorithms to phylogenetics, an approach used by biologists to investigate evolutionary relationships among organisms. For datasets larger than 20-30 species, exhaustive search is not practical in this domain. Gaphyl uses an evolutionary search mechanism to search the space of possible phylogenetic trees, in an attempt to find the most plausible evolutionary hypotheses, while typical phylogenetic software packages use heuristic search methods. In previous work, Gaphyl has been shown to be a promising approach for searching for phylogenetic trees using data with binary attributes and Wagner parsimony to evaluate the trees. In the work reported here, Gaphyl is extended to work with genetic data. Initial results with this extension further suggest that evolutionary search is a promising approach for phylogenetic work.
{"title":"Phylogenetic trees using evolutionary search: initial progress in extending Gaphyl to work with genetic data","authors":"C. Congdon, Kevin J. Septor","doi":"10.1109/CEC.2003.1299592","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299592","url":null,"abstract":"Gaphyl is an application of evolutionary algorithms to phylogenetics, an approach used by biologists to investigate evolutionary relationships among organisms. For datasets larger than 20-30 species, exhaustive search is not practical in this domain. Gaphyl uses an evolutionary search mechanism to search the space of possible phylogenetic trees, in an attempt to find the most plausible evolutionary hypotheses, while typical phylogenetic software packages use heuristic search methods. In previous work, Gaphyl has been shown to be a promising approach for searching for phylogenetic trees using data with binary attributes and Wagner parsimony to evaluate the trees. In the work reported here, Gaphyl is extended to work with genetic data. Initial results with this extension further suggest that evolutionary search is a promising approach for phylogenetic work.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"45 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":"124741918","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.2003.1299826
I. Watanabe, Shouichi Matsui
The performance of ant colony optimization (ACO) algorithms with candidate sets is high for large optimization problems, but it is difficult to set the size of candidate sets to optimal in advance. We propose an adaptive control mechanism of candidate sets based on pheromone concentrations for improving the performance of ACO algorithms and report the results of computational experiments using the graph coloring problems.
{"title":"Improving the performance of ACO algorithms by adaptive control of candidate set","authors":"I. Watanabe, Shouichi Matsui","doi":"10.1109/CEC.2003.1299826","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299826","url":null,"abstract":"The performance of ant colony optimization (ACO) algorithms with candidate sets is high for large optimization problems, but it is difficult to set the size of candidate sets to optimal in advance. We propose an adaptive control mechanism of candidate sets based on pheromone concentrations for improving the performance of ACO algorithms and report the results of computational experiments using the graph coloring problems.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"37 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":"129438334","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.2003.1299926
V. Spais, L. Petrou
A technique is proposed for integrating a probabilistic graph construction algorithm with an evolutionary multiobjective optimizer. A hybrid planner (EvoVBPR) for nonholonic robotic vehicle is presented. It integrates a probabilistic roadmap construction method (VBPR) with the SPEA2 evolutionary multiobjective algorithm and an additional deterministic graph pruning step. The result is a Pareto set of roadmaps that represent different tradeoffs between length of path and obstacle clearance.
{"title":"Multiobective motion planning for a nonholonic vehicle","authors":"V. Spais, L. Petrou","doi":"10.1109/CEC.2003.1299926","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299926","url":null,"abstract":"A technique is proposed for integrating a probabilistic graph construction algorithm with an evolutionary multiobjective optimizer. A hybrid planner (EvoVBPR) for nonholonic robotic vehicle is presented. It integrates a probabilistic roadmap construction method (VBPR) with the SPEA2 evolutionary multiobjective algorithm and an additional deterministic graph pruning step. The result is a Pareto set of roadmaps that represent different tradeoffs between length of path and obstacle clearance.","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":"129852973","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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