The Metrics Apprentice processes a domain knowledge base of software quality concepts with a form of evolutionary computation in order to learn software metrics for a delimited application or software development environment. The evolutionary computation method that is used, the Cultural Algorithm, uses beliefs about the performance of individual population members in order to enhance the evolutionary learning process. In the Metrics Apprentice, these beliefs are an integrated part of the domain knowledge base, and the ones that are most useful in the learning process persist for reuse in future learning tasks. The semantic network that encodes the domain of software quality issues and concepts is displayed using an extension of expandable outlines called the Outline Knowledge Display.
{"title":"The Metrics Apprentice: using cultural algorithms to formulate quality metrics for software systems","authors":"G. S. Cowan, R. Reynolds","doi":"10.1109/CEC.1999.785474","DOIUrl":"https://doi.org/10.1109/CEC.1999.785474","url":null,"abstract":"The Metrics Apprentice processes a domain knowledge base of software quality concepts with a form of evolutionary computation in order to learn software metrics for a delimited application or software development environment. The evolutionary computation method that is used, the Cultural Algorithm, uses beliefs about the performance of individual population members in order to enhance the evolutionary learning process. In the Metrics Apprentice, these beliefs are an integrated part of the domain knowledge base, and the ones that are most useful in the learning process persist for reuse in future learning tasks. The semantic network that encodes the domain of software quality issues and concepts is displayed using an extension of expandable outlines called the Outline Knowledge Display.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"137 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":"130499508","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 documents the discovery of a new, better-than-classical quantum algorithm for the depth-two AND/OR tree problem. We describe the genetic programming system that was constructed specifically for this work, the quantum computer simulator that is used to evaluate the fitness of evolving quantum algorithms, and the newly discovered algorithm.
{"title":"Finding a better-than-classical quantum AND/OR algorithm using genetic programming","authors":"L. Spector, H. Barnum, H. Bernstein, N. Swamy","doi":"10.1109/CEC.1999.785553","DOIUrl":"https://doi.org/10.1109/CEC.1999.785553","url":null,"abstract":"This paper documents the discovery of a new, better-than-classical quantum algorithm for the depth-two AND/OR tree problem. We describe the genetic programming system that was constructed specifically for this work, the quantum computer simulator that is used to evaluate the fitness of evolving quantum algorithms, and the newly discovered algorithm.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"67 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":"130710632","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}
Job rotation is one method that is sometimes used to reduce exposure to strenuous material handling, however, developing effective rotation schedules can be complex in even moderate size facilities. The purpose of this research is to develop methods of incorporating safety criteria into scheduling algorithms to produce job rotation schedules that reduce the potential for injury. Integer programming and a genetic algorithm were used to construct job rotation schedules. Schedules were comprised of lifting tasks whose potential for causing injury was assessed with the Job Severity Index. Each method was used to design four job rotation schedules that met specified safety criteria in a working environment where the object weight, horizontal distance, and repetition rate varied over time. Each rotation was assigned to a specific gender/lifting capacity group. The advantages and limitations of these approaches in developing administrative controls for the prevention of back injury are discussed.
{"title":"A genetic algorithm for designing job rotation schedules considering ergonomic constraints","authors":"B. Carnahan, M. Redfern, B. Norman","doi":"10.1109/CEC.1999.782544","DOIUrl":"https://doi.org/10.1109/CEC.1999.782544","url":null,"abstract":"Job rotation is one method that is sometimes used to reduce exposure to strenuous material handling, however, developing effective rotation schedules can be complex in even moderate size facilities. The purpose of this research is to develop methods of incorporating safety criteria into scheduling algorithms to produce job rotation schedules that reduce the potential for injury. Integer programming and a genetic algorithm were used to construct job rotation schedules. Schedules were comprised of lifting tasks whose potential for causing injury was assessed with the Job Severity Index. Each method was used to design four job rotation schedules that met specified safety criteria in a working environment where the object weight, horizontal distance, and repetition rate varied over time. Each rotation was assigned to a specific gender/lifting capacity group. The advantages and limitations of these approaches in developing administrative controls for the prevention of back injury are discussed.","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":"130806480","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}
Numerous evolutionary computation (EC) courses have been offered at many universities all over the world from the early 90's. However, the field of evolutionary computation is still relatively young, without any standard text nor any standard teaching method. The authors share some experiences in teaching evolutionary courses.
{"title":"Teaching evolutionary algorithms","authors":"Z. Michalewicz, M. Michalewicz","doi":"10.1109/CEC.1999.785479","DOIUrl":"https://doi.org/10.1109/CEC.1999.785479","url":null,"abstract":"Numerous evolutionary computation (EC) courses have been offered at many universities all over the world from the early 90's. However, the field of evolutionary computation is still relatively young, without any standard text nor any standard teaching method. The authors share some experiences in teaching evolutionary courses.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"37 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":"116799453","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}
Previous studies have shown that embedding local search in classical evolutionary programming (EP) could lead to improved performance on function optimization problems. The utility of local search is investigated with fast evolutionary programming (FEP) and comparisons are offered between performance improvements obtained when using local search with Gaussian and Cauchy mutations. Experiments were conducted on a suite of four well known function optimization problems using two local search methods (conjugate gradient and F.J. Solis and R.J.-B. Wets, (1981)) with varying amounts of local search being incorporated into the evolutionary algorithm. Empirical results indicate that FEP with the conjugate gradient method outperforms other hybrid methods on three of the four functions when evolution was conducted for a fixed number of generations. Trials using local search produced solutions that were statistically as good as or better than trials without local search. However, the cost of using local search justified the enhancement in solution quality only when using Gaussian mutations but not when using Cauchy mutations.
{"title":"Local search operators in fast evolutionary programming","authors":"H. K. Birru, K. Chellapilla, S. Rao","doi":"10.1109/CEC.1999.782662","DOIUrl":"https://doi.org/10.1109/CEC.1999.782662","url":null,"abstract":"Previous studies have shown that embedding local search in classical evolutionary programming (EP) could lead to improved performance on function optimization problems. The utility of local search is investigated with fast evolutionary programming (FEP) and comparisons are offered between performance improvements obtained when using local search with Gaussian and Cauchy mutations. Experiments were conducted on a suite of four well known function optimization problems using two local search methods (conjugate gradient and F.J. Solis and R.J.-B. Wets, (1981)) with varying amounts of local search being incorporated into the evolutionary algorithm. Empirical results indicate that FEP with the conjugate gradient method outperforms other hybrid methods on three of the four functions when evolution was conducted for a fixed number of generations. Trials using local search produced solutions that were statistically as good as or better than trials without local search. However, the cost of using local search justified the enhancement in solution quality only when using Gaussian mutations but not when using Cauchy mutations.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"38 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":"117281371","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 problem of immense importance in computational biology is the determination of the functional conformations of protein molecules. With the advent of faster computers, it is now possible to use rules to search conformation space for protein structures that have minimal free energy. The paper surveys work done in the last five years (1994-99) using evolutionary search algorithms to find low energy protein conformations. In particular, a detailed description is included of some work recently started at the National Cancer Institute, which uses evolution strategies.
{"title":"A survey of recent work on evolutionary approaches to the protein folding problem","authors":"G. Greenwood, J. Shin, Byungkook Lee, G. Fogel","doi":"10.1109/CEC.1999.781969","DOIUrl":"https://doi.org/10.1109/CEC.1999.781969","url":null,"abstract":"A problem of immense importance in computational biology is the determination of the functional conformations of protein molecules. With the advent of faster computers, it is now possible to use rules to search conformation space for protein structures that have minimal free energy. The paper surveys work done in the last five years (1994-99) using evolutionary search algorithms to find low energy protein conformations. In particular, a detailed description is included of some work recently started at the National Cancer Institute, which uses evolution strategies.","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":"131218877","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 body of theoretical results regarding conservation of information ("no free lunch") in optimization has not related directly to evolutionary computation. Prior work has assumed that an optimizer traverses a sequence of points in the domain of a function without revisiting points. The present work reduces the difference between theory and practice by a) allowing points to be revisited, b) reasoning about the set of visited points instead of the sequence, and c) considering the impact of bounded memory and revisited points upon optimizer performance. Fortuitously, this leads to clarification of the fundamental results in conservation of information. Although most work in this area emphasizes the futility of attempting to design a generally superior optimizer, the present work highlights possible constructive use of the theory in restricted problem domains.
{"title":"Some information theoretic results on evolutionary optimization","authors":"T. M. English","doi":"10.1109/CEC.1999.782013","DOIUrl":"https://doi.org/10.1109/CEC.1999.782013","url":null,"abstract":"The body of theoretical results regarding conservation of information (\"no free lunch\") in optimization has not related directly to evolutionary computation. Prior work has assumed that an optimizer traverses a sequence of points in the domain of a function without revisiting points. The present work reduces the difference between theory and practice by a) allowing points to be revisited, b) reasoning about the set of visited points instead of the sequence, and c) considering the impact of bounded memory and revisited points upon optimizer performance. Fortuitously, this leads to clarification of the fundamental results in conservation of information. Although most work in this area emphasizes the futility of attempting to design a generally superior optimizer, the present work highlights possible constructive use of the theory in restricted problem domains.","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":"131259324","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}
Gene duplication theory was first proposed by a Japanese biologist, Dr. Susumu Ohno, in the 1970s. Inspired by the theory, we develop a gene duplicated genetic algorithm. Several variants for this algorithm are considered. Individuals with various lengths of genes are evolved based on a parameter-free genetic algorithm and then genes with different lengths are concatenated by migrating among subpopulations. To verify the effectiveness of the gene duplicated genetic algorithm, we performed a comparative study using function optimization problems from the first ICEO (International Contest on Evolutionary Optimization) held in 1996.
{"title":"Genetic algorithm inspired by gene duplication","authors":"H. Sawai, Susumu Adachi","doi":"10.1109/CEC.1999.781967","DOIUrl":"https://doi.org/10.1109/CEC.1999.781967","url":null,"abstract":"Gene duplication theory was first proposed by a Japanese biologist, Dr. Susumu Ohno, in the 1970s. Inspired by the theory, we develop a gene duplicated genetic algorithm. Several variants for this algorithm are considered. Individuals with various lengths of genes are evolved based on a parameter-free genetic algorithm and then genes with different lengths are concatenated by migrating among subpopulations. To verify the effectiveness of the gene duplicated genetic algorithm, we performed a comparative study using function optimization problems from the first ICEO (International Contest on Evolutionary Optimization) held in 1996.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"73 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":"132810877","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 introduces the basic concepts and principles behind quantum computing and examines in detail Shor's (1994) quantum algorithm for factoring very large numbers. Some basic methodological principles and guidelines for constructing quantum algorithms are stated. The aim is not to provide a formal exposition of quantum computing but to identify its novelty and potential use in tackling NP-hard problems.
{"title":"Quantum computing for beginners","authors":"A. Narayanan","doi":"10.1109/CEC.1999.785552","DOIUrl":"https://doi.org/10.1109/CEC.1999.785552","url":null,"abstract":"The paper introduces the basic concepts and principles behind quantum computing and examines in detail Shor's (1994) quantum algorithm for factoring very large numbers. Some basic methodological principles and guidelines for constructing quantum algorithms are stated. The aim is not to provide a formal exposition of quantum computing but to identify its novelty and potential use in tackling NP-hard problems.","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":"128187729","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 proposes a modified genetic algorithm for global minimization. The algorithm uses a new genetic operator, the Mendel operator. This algorithm finds one of the local minimizers first and then finds a lower minimizer at the next iteration as a tunneling algorithm or a filled function method. By repeating these processes, a global minimizer can finally be obtained. Mendel operations simulating Mendel's genetic law are devised to avoid converging to the same minimizer of the previous run. Also, the proposed algorithm guarantees convergence to a lower minimizer by using an elitist method.
{"title":"A genetic algorithm with a Mendel operator for global minimization","authors":"In-Soo Song, Hyun-Wook Woo, M. Tahk","doi":"10.1109/CEC.1999.782664","DOIUrl":"https://doi.org/10.1109/CEC.1999.782664","url":null,"abstract":"This paper proposes a modified genetic algorithm for global minimization. The algorithm uses a new genetic operator, the Mendel operator. This algorithm finds one of the local minimizers first and then finds a lower minimizer at the next iteration as a tunneling algorithm or a filled function method. By repeating these processes, a global minimizer can finally be obtained. Mendel operations simulating Mendel's genetic law are devised to avoid converging to the same minimizer of the previous run. Also, the proposed algorithm guarantees convergence to a lower minimizer by using an elitist method.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"290 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":"134494228","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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