Pub Date : 2002-08-07DOI: 10.1109/CEC.2002.1004461
Ji Hun Koo, Tae-Seon Kim, S. Dong, Chong-Ho Lee
In this paper, a genetic algorithm-based adaptive image enhancement filtering scheme is proposed and implemented on an FPGA board. In contrast to conventional filter systems, the proposed system can find an optimal combination of filters, as well as their sequent order and parameter values, adaptively under unknown noise types using structured genetic algorithms. For evaluation, three types of noise were used, and the experimental results showed that the proposed scheme can generate an optimal set of filters adaptively without a-priori noise information.
{"title":"Development of FPGA based adaptive image enhancement filter system using genetic algorithms","authors":"Ji Hun Koo, Tae-Seon Kim, S. Dong, Chong-Ho Lee","doi":"10.1109/CEC.2002.1004461","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004461","url":null,"abstract":"In this paper, a genetic algorithm-based adaptive image enhancement filtering scheme is proposed and implemented on an FPGA board. In contrast to conventional filter systems, the proposed system can find an optimal combination of filters, as well as their sequent order and parameter values, adaptively under unknown noise types using structured genetic algorithms. For evaluation, three types of noise were used, and the experimental results showed that the proposed scheme can generate an optimal set of filters adaptively without a-priori noise information.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"3 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114037131","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 : 2002-05-12DOI: 10.1109/CEC.2002.1004453
T. Satoh, H. Kuwabara, M. Kanezashi, K. Nara
This paper presents a distributed algorithm for minimizing a nonconvex multimodal function. In recent years, new distributed algorithms based on an artificial life (A Life) system have been studied and its potential power has been demonstrated. In this paper, therefore, the framework of an ALife system is employed into a function minimization. Since the proposed method utilizes no gradient information, it can be applied to a very wide class of optimization problems. The effectiveness of the proposed method is demonstrated through the practical multi-dimensional problem, the so called multiple-fuel economic load dispatch problem.
{"title":"Artificial life system and its application to multiple-fuel economic load dispatch problem","authors":"T. Satoh, H. Kuwabara, M. Kanezashi, K. Nara","doi":"10.1109/CEC.2002.1004453","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004453","url":null,"abstract":"This paper presents a distributed algorithm for minimizing a nonconvex multimodal function. In recent years, new distributed algorithms based on an artificial life (A Life) system have been studied and its potential power has been demonstrated. In this paper, therefore, the framework of an ALife system is employed into a function minimization. Since the proposed method utilizes no gradient information, it can be applied to a very wide class of optimization problems. The effectiveness of the proposed method is demonstrated through the practical multi-dimensional problem, the so called multiple-fuel economic load dispatch problem.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115598531","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 : 2002-05-12DOI: 10.1109/CEC.2002.1004394
Timothy E. Revello, R. McCartney
Unlike most games which have fixed rules, the rules for war games can contain uncertainty. This uncertainty makes war games difficult to address with methods typically used for playing games by machine. The characteristics of war games match well with the domain for which genetic algorithms are effective. We explore the use of genetic algorithms for generating war game strategies.
{"title":"Generating war game strategies using a genetic algorithm","authors":"Timothy E. Revello, R. McCartney","doi":"10.1109/CEC.2002.1004394","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004394","url":null,"abstract":"Unlike most games which have fixed rules, the rules for war games can contain uncertainty. This uncertainty makes war games difficult to address with methods typically used for playing games by machine. The characteristics of war games match well with the domain for which genetic algorithms are effective. We explore the use of genetic algorithms for generating war game strategies.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"557 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116647892","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 : 2002-05-12DOI: 10.1109/CEC.2002.1006279
Renato Reder Cazangi, M. Figueiredo
An evolutionary autonomous navigation system is described that evolves two basic, conflicting behaviors, namely, obstacle avoidance and target seeking, as the system acquires skill to coordinate them (behavior emergence and coordination skill acquisition happen simultaneously). Simulation experiments show promising results: the number of target captures increases and the number of collisions stabilizes, as generations proceed. They confirm the evolutionary learning capacity of the reactive navigation system proposed.
{"title":"Simultaneous emergence of conflicting basic behaviors and their coordination in an evolutionary autonomous navigation system","authors":"Renato Reder Cazangi, M. Figueiredo","doi":"10.1109/CEC.2002.1006279","DOIUrl":"https://doi.org/10.1109/CEC.2002.1006279","url":null,"abstract":"An evolutionary autonomous navigation system is described that evolves two basic, conflicting behaviors, namely, obstacle avoidance and target seeking, as the system acquires skill to coordinate them (behavior emergence and coordination skill acquisition happen simultaneously). Simulation experiments show promising results: the number of target captures increases and the number of collisions stabilizes, as generations proceed. They confirm the evolutionary learning capacity of the reactive navigation system proposed.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116898375","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 : 2002-05-12DOI: 10.1109/CEC.2002.1006276
C. Ho, Y. P. Singh, Sze Wei Lee
The simulated annealing (SA) method is applied to the initial population of a genetic algorithm (GA) that was designed to construct optical orthogonal codes. This has improved the quality of the initial population, with an increase in the average fitness value and the number of above-average individuals. This in turn, has enabled the genetic algorithm to converge faster.
{"title":"Enhancement of a genetic algorithm for optical orthogonal code design using simulated annealing","authors":"C. Ho, Y. P. Singh, Sze Wei Lee","doi":"10.1109/CEC.2002.1006276","DOIUrl":"https://doi.org/10.1109/CEC.2002.1006276","url":null,"abstract":"The simulated annealing (SA) method is applied to the initial population of a genetic algorithm (GA) that was designed to construct optical orthogonal codes. This has improved the quality of the initial population, with an increase in the average fitness value and the number of above-average individuals. This in turn, has enabled the genetic algorithm to converge faster.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"340 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120932926","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 : 2002-05-12DOI: 10.1109/CEC.2002.1004475
Michael Schreyer, G. Raidl
This paper describes an ant colony system (ACS) for labeling point features. A pre-processing step reduces the search space in a safe way. The ACS applies local improvement and masking, a technique that focuses the optimization on critical regions. Empirical results indicate that the ACS reliably identifies high-quality solutions which are in many cases better than those of a state-of-the-art genetic algorithm for point-feature labeling.
{"title":"Letting ants labeling point features [sic.: for 'labeling' read 'label']","authors":"Michael Schreyer, G. Raidl","doi":"10.1109/CEC.2002.1004475","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004475","url":null,"abstract":"This paper describes an ant colony system (ACS) for labeling point features. A pre-processing step reduces the search space in a safe way. The ACS applies local improvement and masking, a technique that focuses the optimization on critical regions. Empirical results indicate that the ACS reliably identifies high-quality solutions which are in many cases better than those of a state-of-the-art genetic algorithm for point-feature labeling.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121095749","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 : 2002-05-12DOI: 10.1109/CEC.2002.1006221
R. O. Day, J. Zydallis, G. Lamont, R. Pachter
This paper presents two methods designed to improve the efficiency and effectiveness of the parallel fast messy GA used in solving the Protein Structure Prediction (PSP) problem. The first is an application of a farming model - targeting algorithm efficiency. The second successful method addresses the building block sizes used in the algorithm - targeting algorithm effectiveness.
{"title":"Analysis of fine granularity and building block sizes in the parallel fast messy GA","authors":"R. O. Day, J. Zydallis, G. Lamont, R. Pachter","doi":"10.1109/CEC.2002.1006221","DOIUrl":"https://doi.org/10.1109/CEC.2002.1006221","url":null,"abstract":"This paper presents two methods designed to improve the efficiency and effectiveness of the parallel fast messy GA used in solving the Protein Structure Prediction (PSP) problem. The first is an application of a farming model - targeting algorithm efficiency. The second successful method addresses the building block sizes used in the algorithm - targeting algorithm effectiveness.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121127305","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 : 2002-05-12DOI: 10.1109/CEC.2002.1004387
Justin Balthrop, S. Forrest, Matthew R. Glickman
This paper studies a simplified form of LISYS, an artificial immune system for network intrusion detection. The paper describes results based on a new, more controlled data set than that used for earlier studies. The paper also looks at which parameters appear most important for minimizing false positives, as well as the trade-offs and relationships among parameter settings.
{"title":"Revisiting LISYS: parameters and normal behavior","authors":"Justin Balthrop, S. Forrest, Matthew R. Glickman","doi":"10.1109/CEC.2002.1004387","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004387","url":null,"abstract":"This paper studies a simplified form of LISYS, an artificial immune system for network intrusion detection. The paper describes results based on a new, more controlled data set than that used for earlier studies. The paper also looks at which parameters appear most important for minimizing false positives, as well as the trade-offs and relationships among parameter settings.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121334897","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 : 2002-05-12DOI: 10.1109/CEC.2002.1004402
F. Jiménez, A. Gómez-Skarmeta, Gracia Sánchez, K. Deb
The paper follows the line of the design and evaluation of new evolutionary algorithms for constrained multi-objective optimization. The evolutionary algorithm proposed (ENORA) incorporates the Pareto concept of multi-objective optimization with a constraint handling technique and with a powerful diversity mechanism to obtain multiple nondominated solutions through the simple run of the algorithm. Constraint handling is carried out in an evolutionary way and using the min-max formulation, while the diversity technique is based on the partitioning of search space in a set of radial slots along which are positioned the successive populations generated by the algorithm. A set of test problems recently proposed for the evaluation of this kind of algorithm has been used in the evaluation of the algorithm presented. The results obtained with ENORA were very good and considerably better than those obtained with algorithms recently proposed by other authors.
{"title":"An evolutionary algorithm for constrained multi-objective optimization","authors":"F. Jiménez, A. Gómez-Skarmeta, Gracia Sánchez, K. Deb","doi":"10.1109/CEC.2002.1004402","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004402","url":null,"abstract":"The paper follows the line of the design and evaluation of new evolutionary algorithms for constrained multi-objective optimization. The evolutionary algorithm proposed (ENORA) incorporates the Pareto concept of multi-objective optimization with a constraint handling technique and with a powerful diversity mechanism to obtain multiple nondominated solutions through the simple run of the algorithm. Constraint handling is carried out in an evolutionary way and using the min-max formulation, while the diversity technique is based on the partitioning of search space in a set of radial slots along which are positioned the successive populations generated by the algorithm. A set of test problems recently proposed for the evaluation of this kind of algorithm has been used in the evaluation of the algorithm presented. The results obtained with ENORA were very good and considerably better than those obtained with algorithms recently proposed by other authors.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126186346","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 : 2002-05-12DOI: 10.1109/CEC.2002.1007053
H. Ishibuchi, Tadsahi Yoshida, T. Murata
In multiobjective genetic local search (MOGLS) algorithms, the local search is usually applied to all offsprings generated by genetic operations. This paper proposes an idea of selecting only good offsprings as initial solutions for the local search. Simulation results show that the proposed idea significantly improves the search ability of MOGLS algorithms.
{"title":"Selection of initial solutions for local search in multiobjective genetic local search","authors":"H. Ishibuchi, Tadsahi Yoshida, T. Murata","doi":"10.1109/CEC.2002.1007053","DOIUrl":"https://doi.org/10.1109/CEC.2002.1007053","url":null,"abstract":"In multiobjective genetic local search (MOGLS) algorithms, the local search is usually applied to all offsprings generated by genetic operations. This paper proposes an idea of selecting only good offsprings as initial solutions for the local search. Simulation results show that the proposed idea significantly improves the search ability of MOGLS algorithms.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123338032","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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