Transposition is a new genetic operator alternative to crossover and allows a classical GA to achieve better results. This mechanism characterized by the presence of mobile genetic units must be used with the right parameters to enable maximum performance to the GA. The paper presents the results of an empirical study which offers the main guidelines to choose the proper setting of parameters to use with transposition, which will lead the GA to the best solutions.
{"title":"Enhancing transposition performance","authors":"A. Simoes, E. Costa","doi":"10.1109/CEC.1999.782651","DOIUrl":"https://doi.org/10.1109/CEC.1999.782651","url":null,"abstract":"Transposition is a new genetic operator alternative to crossover and allows a classical GA to achieve better results. This mechanism characterized by the presence of mobile genetic units must be used with the right parameters to enable maximum performance to the GA. The paper presents the results of an empirical study which offers the main guidelines to choose the proper setting of parameters to use with transposition, which will lead the GA to the best solutions.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"35 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":"115235808","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}
The paper provides a preliminary evaluation of the accuracy of computer aided diagnostics (CAD) in addressing the inconsistencies of inter-observer variance scoring. The inter-observer variability problem, in this case, relates to different cytopathologists and radiologists at separate locations scoring the same type of samples differently using the same methodologies and environmental discriminates. Two distinctly different FNA data sets were used. The first was the data collected at the University of Wisconsin (Wolberg data set) while the other was a completely independent one defined and processed at the Breast Cancer Center, University Health Center at Syracuse (Syracuse data set). Two computer aided diagnostic (CAD) paradigms were used: the evolutionary programming (EP)/probabilistic neural network (PNN) hybrid and a mean of predictors model. Four experiments mere performed to evaluate the hybrid. The fourth experiment, k-fold crossover validation, resulted in a 91.25% average classification accuracy with a .9783 average Az index. The mean of predictors model was used to verify the results of the more complex hybrid using both the fraction of missed malignancies (Type II errors) and fraction of false malignancies (Type I errors). The EP/PNN hybrid experiments resulted in a 3.05% mean value of missed malignancies (Type II) and a 5.69% mean value of false malignancies (Type I errors) using the k-fold crossover studies. The mean of predictors model provided a.429% mean Type II error and a 4.09% mean Type I error.
{"title":"Investigation of and preliminary results for the solution of the inter-observer variability problem using fine needle aspirate (FNA) data","authors":"W. Land, Lewis A. Loren, T. Masters","doi":"10.1109/CEC.1999.785489","DOIUrl":"https://doi.org/10.1109/CEC.1999.785489","url":null,"abstract":"The paper provides a preliminary evaluation of the accuracy of computer aided diagnostics (CAD) in addressing the inconsistencies of inter-observer variance scoring. The inter-observer variability problem, in this case, relates to different cytopathologists and radiologists at separate locations scoring the same type of samples differently using the same methodologies and environmental discriminates. Two distinctly different FNA data sets were used. The first was the data collected at the University of Wisconsin (Wolberg data set) while the other was a completely independent one defined and processed at the Breast Cancer Center, University Health Center at Syracuse (Syracuse data set). Two computer aided diagnostic (CAD) paradigms were used: the evolutionary programming (EP)/probabilistic neural network (PNN) hybrid and a mean of predictors model. Four experiments mere performed to evaluate the hybrid. The fourth experiment, k-fold crossover validation, resulted in a 91.25% average classification accuracy with a .9783 average Az index. The mean of predictors model was used to verify the results of the more complex hybrid using both the fraction of missed malignancies (Type II errors) and fraction of false malignancies (Type I errors). The EP/PNN hybrid experiments resulted in a 3.05% mean value of missed malignancies (Type II) and a 5.69% mean value of false malignancies (Type I errors) using the k-fold crossover studies. The mean of predictors model provided a.429% mean Type II error and a 4.09% mean Type I error.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"55 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":"115537690","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 fuzzy simulated evolution algorithm is presented for multi-objective minimization of VLSI cell placement problem. We propose a fuzzy goal-based search strategy combined with a fuzzy allocation scheme. The allocation scheme tries to minimize multiple objectives and adds controlled randomness as opposed to original deterministic allocation schemes. Experiments with benchmark tests demonstrate a noticeable improvement in solution quality.
{"title":"Fuzzy simulated evolution algorithm for multi-objective optimization of VLSI placement","authors":"S. M. Sait, H. Youssef, Hussain Ali","doi":"10.1109/CEC.1999.781912","DOIUrl":"https://doi.org/10.1109/CEC.1999.781912","url":null,"abstract":"A fuzzy simulated evolution algorithm is presented for multi-objective minimization of VLSI cell placement problem. We propose a fuzzy goal-based search strategy combined with a fuzzy allocation scheme. The allocation scheme tries to minimize multiple objectives and adds controlled randomness as opposed to original deterministic allocation schemes. Experiments with benchmark tests demonstrate a noticeable improvement in solution quality.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"22 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":"123565504","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}
Sequencing of DNA is among the most important tasks in molecular biology. DNA chips are considered to be a more rapid alternative to more common gel-based methods of sequencing. Previously, we demonstrated the reconstruction of DNA sequence information from a simulated DNA chip using evolutionary programming. The research presented here extends this work by relaxing several assumptions adopted in our initial investigation. We also examine the relationship between base composition of the target sequence and the useful set of probes required to decipher the target on a DNA chip. Comments regarding the nature of the optimal ratio for the target and probe lengths are offered. Our results go further to suggest that evolutionary computation is well-suited to address the sequence reconstruction problem.
{"title":"Simulated sequencing by hybridization using evolutionary programming","authors":"G. Fogel, K. Chellapilla","doi":"10.1109/CEC.1999.781960","DOIUrl":"https://doi.org/10.1109/CEC.1999.781960","url":null,"abstract":"Sequencing of DNA is among the most important tasks in molecular biology. DNA chips are considered to be a more rapid alternative to more common gel-based methods of sequencing. Previously, we demonstrated the reconstruction of DNA sequence information from a simulated DNA chip using evolutionary programming. The research presented here extends this work by relaxing several assumptions adopted in our initial investigation. We also examine the relationship between base composition of the target sequence and the useful set of probes required to decipher the target on a DNA chip. Comments regarding the nature of the optimal ratio for the target and probe lengths are offered. Our results go further to suggest that evolutionary computation is well-suited to address the sequence reconstruction problem.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"35 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":"125032748","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}
J. Ortega, J. Bernier, A. F. Díaz, I. Rojas, M. Salmerón, A. Prieto
An evolutionary computation approach is used to learn online the rules that allow the processors in a parallel platform to cooperate by interchanging the local optima that they find while they concurrently explore different zones of the solution space. The cooperation of processors can greatly benefit the resolution of combinatorial optimization problems by decreasing their runtimes, by increasing the quality of the solutions obtained, or both. Moreover, as parallel computers are more and more accessible, the application of parallel processing to solve these problems becomes a practical and interesting alternative. As an example, a parallel optimization algorithm based on Boltzmann Machine has been used for a detailed description and evaluation of the proposed cooperation approach.
{"title":"Parallel combinatorial optimization with evolutionary cooperation between processors","authors":"J. Ortega, J. Bernier, A. F. Díaz, I. Rojas, M. Salmerón, A. Prieto","doi":"10.1109/CEC.1999.782539","DOIUrl":"https://doi.org/10.1109/CEC.1999.782539","url":null,"abstract":"An evolutionary computation approach is used to learn online the rules that allow the processors in a parallel platform to cooperate by interchanging the local optima that they find while they concurrently explore different zones of the solution space. The cooperation of processors can greatly benefit the resolution of combinatorial optimization problems by decreasing their runtimes, by increasing the quality of the solutions obtained, or both. Moreover, as parallel computers are more and more accessible, the application of parallel processing to solve these problems becomes a practical and interesting alternative. As an example, a parallel optimization algorithm based on Boltzmann Machine has been used for a detailed description and evaluation of the proposed cooperation approach.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"9 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":"129057627","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}
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 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}
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