Describes an application of genetic algorithms (GAs) to classify epidemiological data, which is often challenging to classify due to noise and other factors. For such complex data (that requires a large number of very specific rules in order to achieve high accuracy), smaller rule sets, composed of more general rules, may be preferable, even if they are less accurate. The GA presented in this paper allows the user to encourage smaller rule sets by setting a parameter. The rule sets found are also compared to those created by standard decision-tree algorithms. The results illustrate tradeoffs involving the number of rules, descriptive accuracy, predictive accuracy, and accuracy in describing and predicting positive examples across different rule sets.
{"title":"Classification of epidemiological data: a comparison of genetic algorithm and decision tree approaches","authors":"C. Congdon","doi":"10.1109/CEC.2000.870330","DOIUrl":"https://doi.org/10.1109/CEC.2000.870330","url":null,"abstract":"Describes an application of genetic algorithms (GAs) to classify epidemiological data, which is often challenging to classify due to noise and other factors. For such complex data (that requires a large number of very specific rules in order to achieve high accuracy), smaller rule sets, composed of more general rules, may be preferable, even if they are less accurate. The GA presented in this paper allows the user to encourage smaller rule sets by setting a parameter. The rule sets found are also compared to those created by standard decision-tree algorithms. The results illustrate tradeoffs involving the number of rules, descriptive accuracy, predictive accuracy, and accuracy in describing and predicting positive examples across different rule sets.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129244156","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}
I. D. Falco, A. Iazzetta, E. Tarantino, Antonio Della Cioppa
The search for novel and useful patterns within large databases, known as data mining, has become of great importance owing to the ever-increasing amounts of data collected by large organizations. In particular, the emphasis is on heuristic search methods which are able to discover patterns that are hard or impossible to detect using standard query mechanisms and classical statistical techniques. In this paper, an evolutionary system that is capable of extracting explicit classification rules is presented. The results are compared with those obtained by other approaches.
{"title":"An evolutionary system for automatic explicit rule extraction","authors":"I. D. Falco, A. Iazzetta, E. Tarantino, Antonio Della Cioppa","doi":"10.1109/CEC.2000.870331","DOIUrl":"https://doi.org/10.1109/CEC.2000.870331","url":null,"abstract":"The search for novel and useful patterns within large databases, known as data mining, has become of great importance owing to the ever-increasing amounts of data collected by large organizations. In particular, the emphasis is on heuristic search methods which are able to discover patterns that are hard or impossible to detect using standard query mechanisms and classical statistical techniques. In this paper, an evolutionary system that is capable of extracting explicit classification rules is presented. The results are compared with those obtained by other approaches.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115967251","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}
This paper presents an evolutionary approach, called the family competition evolutionary algorithm (FCEA), for optical thin film design. The proposed approach, based on family competition and multiple adaptive rules, integrates decreasing-based Gaussian mutation and two self-adaptive mutations to balance the exploitation and exploration. It is implemented and applied to two coating systems. Numerical results indicate that the proposed approach is very robust for optical coatings.
{"title":"A robust evolutionary algorithm for optical thin-film designs","authors":"Jinn-Moon Yang, C. Kao","doi":"10.1109/CEC.2000.870751","DOIUrl":"https://doi.org/10.1109/CEC.2000.870751","url":null,"abstract":"This paper presents an evolutionary approach, called the family competition evolutionary algorithm (FCEA), for optical thin film design. The proposed approach, based on family competition and multiple adaptive rules, integrates decreasing-based Gaussian mutation and two self-adaptive mutations to balance the exploitation and exploration. It is implemented and applied to two coating systems. Numerical results indicate that the proposed approach is very robust for optical coatings.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117289744","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}
This paper introduces a new automatic image enhancement technique based on real-coded genetic algorithms (GAs). The task of the GA is to adapt the parameters of a novel extension to a local enhancement technique similar to statistical scaling, as to enhance the contrast and detail in the image according to an objective fitness criterion. We compared our method with other automatic enhancement techniques, like contrast stretching and histogram equalization methods. Results obtained, both in terms of subjective and objective evaluation, show the superiority of our method.
{"title":"Towards automatic image enhancement using genetic algorithms","authors":"C. Munteanu, Á. Rosa","doi":"10.1109/CEC.2000.870836","DOIUrl":"https://doi.org/10.1109/CEC.2000.870836","url":null,"abstract":"This paper introduces a new automatic image enhancement technique based on real-coded genetic algorithms (GAs). The task of the GA is to adapt the parameters of a novel extension to a local enhancement technique similar to statistical scaling, as to enhance the contrast and detail in the image according to an objective fitness criterion. We compared our method with other automatic enhancement techniques, like contrast stretching and histogram equalization methods. Results obtained, both in terms of subjective and objective evaluation, show the superiority of our method.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117208546","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 attempt to reconstruct Sewall Wright's (1932) shifting balance theory in order to address some of the major criticisms leveled against it. The resulting abstract process is applied to the GA forming the shifting balance genetic algorithm (SBGA), which is shown to behave as Wright intended. For example, the SBGA avoids local optima through a shifting balance between subpopulations, as is demonstrated in an experiment. The experiment also shows that the SBGA outperforms the classical GA in both stationary and changing environments.
{"title":"Reconstructing the shifting balance theory in a GA: taking Sewall Wright seriously","authors":"F. Oppacher, M. Wineberg","doi":"10.1109/CEC.2000.870298","DOIUrl":"https://doi.org/10.1109/CEC.2000.870298","url":null,"abstract":"We attempt to reconstruct Sewall Wright's (1932) shifting balance theory in order to address some of the major criticisms leveled against it. The resulting abstract process is applied to the GA forming the shifting balance genetic algorithm (SBGA), which is shown to behave as Wright intended. For example, the SBGA avoids local optima through a shifting balance between subpopulations, as is demonstrated in an experiment. The experiment also shows that the SBGA outperforms the classical GA in both stationary and changing environments.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115527608","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 effect of population isolation is discussed by means of an analysis of the domains of attraction of local optima. Separation among populations and adaptive gathering of the initial population are achieved by local evolution, so as to transform the multi-modal function optimization into a uni-modal function optimization. Combining the space-division-based (/spl mu/+1) selection approach, which has a rapid convergence speed in uni-modal function optimization, a new evolutionary algorithm is presented to automatically separate and gather the initial population according to its domains of attraction. Numerical simulation results show the global searching ability of the new algorithm.
{"title":"Multi-population adaptive-gathering evolutionary algorithm in function optimization","authors":"Si-Duo Chen, Zhang-can Huang","doi":"10.1109/CEC.2000.870383","DOIUrl":"https://doi.org/10.1109/CEC.2000.870383","url":null,"abstract":"The effect of population isolation is discussed by means of an analysis of the domains of attraction of local optima. Separation among populations and adaptive gathering of the initial population are achieved by local evolution, so as to transform the multi-modal function optimization into a uni-modal function optimization. Combining the space-division-based (/spl mu/+1) selection approach, which has a rapid convergence speed in uni-modal function optimization, a new evolutionary algorithm is presented to automatically separate and gather the initial population according to its domains of attraction. Numerical simulation results show the global searching ability of the new algorithm.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"257 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115790805","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 neutral theory of evolution suggests that most mutations do not cause a phenotypic change. In this case the mapping from genotype to phenotype contains redundancy such that many mutations do not have an appreciable effect on the phenotype. This can result in neutral networks; sets of genotypes connected by single point mutations that map to the same phenotype. A population is able to drift along these networks, eventually encountering phenotypes of higher fitness, thus reducing the chance of becoming trapped in sub-optimal regions of genotype space. In this paper we explore the use and benefit of redundant mappings for evolutionary search. We investigate the properties of several genotype-phenotype mappings by performing random walks along the neutral networks in their genotype spaces. The properties are explored further by performing adaptive walks in which a concept of fitness is introduced. A mapping based on a random Boolean network was found to have particularly interesting properties in both cases.
{"title":"An investigation of redundant genotype-phenotype mappings and their role in evolutionary search","authors":"M. Shackleton, R. Shipma, M. Ebner","doi":"10.1109/CEC.2000.870337","DOIUrl":"https://doi.org/10.1109/CEC.2000.870337","url":null,"abstract":"The neutral theory of evolution suggests that most mutations do not cause a phenotypic change. In this case the mapping from genotype to phenotype contains redundancy such that many mutations do not have an appreciable effect on the phenotype. This can result in neutral networks; sets of genotypes connected by single point mutations that map to the same phenotype. A population is able to drift along these networks, eventually encountering phenotypes of higher fitness, thus reducing the chance of becoming trapped in sub-optimal regions of genotype space. In this paper we explore the use and benefit of redundant mappings for evolutionary search. We investigate the properties of several genotype-phenotype mappings by performing random walks along the neutral networks in their genotype spaces. The properties are explored further by performing adaptive walks in which a concept of fitness is introduced. A mapping based on a random Boolean network was found to have particularly interesting properties in both cases.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125285530","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 describe our research on building a free, evolutionary, Internet-based, agent-based, long-distance teaching environment for academic English. Web English teaching environments are few, and mostly they imply a fee. However, none of them considers the challenges the non-native English-speaking academic has to face. We describe some of the design and implementation aspects of the system prototype, focusing especially on the evolutionary, adaptive features, and only marginally on the pedagogical issues involved.
{"title":"MyEnglishTeacher-an evolutionary Web-based, multi-agent environment for academic English teaching","authors":"A. Cristea, Toshio Okamoto, P. Cristea","doi":"10.1109/CEC.2000.870808","DOIUrl":"https://doi.org/10.1109/CEC.2000.870808","url":null,"abstract":"We describe our research on building a free, evolutionary, Internet-based, agent-based, long-distance teaching environment for academic English. Web English teaching environments are few, and mostly they imply a fee. However, none of them considers the challenges the non-native English-speaking academic has to face. We describe some of the design and implementation aspects of the system prototype, focusing especially on the evolutionary, adaptive features, and only marginally on the pedagogical issues involved.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126755887","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}
Distributed populations in genetic algorithms can make the search more smart, in that local minima may be skipped. However, when the global population is divided into small sub-populations, the ability of these sub-populations to evolve is set back because of their relatively small sizes. In this paper, a new method to manage the distributed populations in evolution is introduced. A supervising subroutine observes all the sub-populations during evolution. The sizes of these sub-populations are dynamically changed according to their performance. Better sub-populations get more quotas of the total number of individuals, thus get more possibility to produce even better ones. This algorithm is illustrated with an example. Different policies of managing the sub-populations are compared and discussed. The main conclusion is that dynamical rearrangement of the global population can make the process of evolution faster and more stable.
{"title":"Dynamic distributed genetic algorithms","authors":"Weilie Yi, Qizhen Liu, Yongbao He","doi":"10.1109/CEC.2000.870775","DOIUrl":"https://doi.org/10.1109/CEC.2000.870775","url":null,"abstract":"Distributed populations in genetic algorithms can make the search more smart, in that local minima may be skipped. However, when the global population is divided into small sub-populations, the ability of these sub-populations to evolve is set back because of their relatively small sizes. In this paper, a new method to manage the distributed populations in evolution is introduced. A supervising subroutine observes all the sub-populations during evolution. The sizes of these sub-populations are dynamically changed according to their performance. Better sub-populations get more quotas of the total number of individuals, thus get more possibility to produce even better ones. This algorithm is illustrated with an example. Different policies of managing the sub-populations are compared and discussed. The main conclusion is that dynamical rearrangement of the global population can make the process of evolution faster and more stable.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126514084","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}
Coevolutionary algorithms have received increased attention in the past few years within the domain of evolutionary computation. We combine the search power of coevolutionary computation with the expressive power of fuzzy systems, introducing a novel algorithm, Fuzzy CoCo: Fuzzy Cooperative Coevolution. We demonstrate the efficacy of Fuzzy CoCo by applying it to a hard, real-world problem-breast cancer diagnosis-obtaining the best results to date while expending less computational effort than formerly.
{"title":"Applying Fuzzy CoCo to breast cancer diagnosis","authors":"C. Peña-Reyes, M. Sipper","doi":"10.1109/CEC.2000.870780","DOIUrl":"https://doi.org/10.1109/CEC.2000.870780","url":null,"abstract":"Coevolutionary algorithms have received increased attention in the past few years within the domain of evolutionary computation. We combine the search power of coevolutionary computation with the expressive power of fuzzy systems, introducing a novel algorithm, Fuzzy CoCo: Fuzzy Cooperative Coevolution. We demonstrate the efficacy of Fuzzy CoCo by applying it to a hard, real-world problem-breast cancer diagnosis-obtaining the best results to date while expending less computational effort than formerly.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124260389","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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