K. Nakano, Shingo Uchihashi, Naoki Umemoto, H. Nakagama
{"title":"An approach to evolutional system","authors":"K. Nakano, Shingo Uchihashi, Naoki Umemoto, H. Nakagama","doi":"10.1109/ICEC.1994.349957","DOIUrl":"https://doi.org/10.1109/ICEC.1994.349957","url":null,"abstract":"Everything on the earth, from chemical compounds to living things, consist of about 100 kinds of chemical elements. Moreover, they are considered to have self-organized. In order to elucidate the mechanism of such self-organization we made a simple model. When self-organization occurs, there must be some information of the structures. We call the information \"system description\". Our model has 5 kinds of elements. If the elements are assembled to a system according to a system description, the system will have a specific function. When two system descriptions mate with each other accompanied by mutations. A new system appears. We devised the format of \"system descriptions\" and the mechanism of producing the structure corresponding to each system description. In our experiment several initial systems repeated mating and mutation during some generations to produce many kinds of systems each of which has its specific function. If natural selection is added to this model, it will be a model for evolution.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128094000","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}
{"title":"Handling deceptive problems using a different genetic search","authors":"D. Dasgupta","doi":"10.1109/ICEC.1994.349952","DOIUrl":"https://doi.org/10.1109/ICEC.1994.349952","url":null,"abstract":"In recent years, several studies have been devoted to the design of problems with different degrees of deception in order to investigate the performance of GAs. This paper presents a different genetic approach, called the structured genetic algorithm (sGA) for solving GA-deceptive problems. The structured GA uses an hierarchical encoding and a gene expression mechanism in its overspecified chromosomal representation. The paper reported some experimental results which demonstrated that on using a different chromosomal representation (as in sGA), the genetic search becomes more robust and can easily handle so-called GA-deceptive problems.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134270934","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}
{"title":"Knowledge-based nonuniform crossover","authors":"H. Maini, K. Mehrotra, C. Mohan, S. Ranka","doi":"10.1109/ICEC.1994.350048","DOIUrl":"https://doi.org/10.1109/ICEC.1994.350048","url":null,"abstract":"One-point, two-point and k-point crossover can be viewed as special cases of uniform crossover, where genetic material is chosen each locus of either parent with equal probability (G. Syswerda, 1989). The paper generalizes uniform crossover to \"non-uniform crossover\" using \"mask\" vectors whose elements are real numbers /spl isin/[0, 1], representing problem-specific knowledge that improves performance by biasing the selection of alleles from either parent. This knowledge based non-uniform crossover (KNUX) is applied to two NP optimization problems: graph partitioning and soft-decision decoding of linear block codes (H.S. Maini, 1993). Simulation results show orders of magnitude improvement of this operator over two-point and uniform crossover. An appropriate schema theorem is also developed.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116412846","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}
{"title":"Varying quality function in genetic algorithms and the cutting problem","authors":"V. Petridis, S. Kazarlis","doi":"10.1109/ICEC.1994.350022","DOIUrl":"https://doi.org/10.1109/ICEC.1994.350022","url":null,"abstract":"In this paper, an implementation of a genetic algorithm (GA) is presented, using a quality function that is not unaltered but changes according to the search evolution. This means that the GA 'sees' a continuously changing search space, throughout one run. The example chosen to test the effect of a varying quality function is the cutting problem. Simulation results show that the dynamic quality function performs much better than its static counterpart.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124658622","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}
{"title":"Application of genetic algorithms to the problem of free-routing for aircraft","authors":"Ingrid S. Gerdes","doi":"10.1109/ICEC.1994.350003","DOIUrl":"https://doi.org/10.1109/ICEC.1994.350003","url":null,"abstract":"Describes an application of genetic algorithms to the problem of aircraft routing in an airspace. A detailed description of the problem and the algorithm used for it are presented. Furthermore, the results of several experiments with a generated flight scenario and a real-traffic scenario are outlined. This paper shows the initial stages in finding a solution to decrease the delay in the airspace and to use the airspace more efficiently.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129168919","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}
{"title":"Optimization of fuzzy clustering criteria using genetic algorithms","authors":"J. Bezdek, R. Hathaway","doi":"10.1109/ICEC.1994.349993","DOIUrl":"https://doi.org/10.1109/ICEC.1994.349993","url":null,"abstract":"This paper introduces a general approach based on genetic algorithms for optimizing a broad class of clustering criteria. The standard approach for optimizing these criteria has been to alternate optimizations between the variables which represent fuzzy memberships of the data to various clusters, and those prototype variables which determine the geometry of the clusters. The approach suggested here first re-parameterizes the criteria into functions of the prototype variables alone. The prototype variables are then coded as binary strings so that genetic algorithms can be applied. An overview of the approach and two simple numerical examples are given.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127743229","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}
{"title":"Scheduling multiple job problems with guided evolutionary simulated annealing approach","authors":"Chang-Yun Shen, Y. Pao, Percy P. C. Yip","doi":"10.1109/ICEC.1994.349972","DOIUrl":"https://doi.org/10.1109/ICEC.1994.349972","url":null,"abstract":"This paper reports on an investigation of whether a special type of evolutionary programming named guided evolutionary simulated annealing (GESA) might be used effectively for dealing with scheduling tasks. The GESA approach allows many candidate solutions to be 'alive' at the same time. There is local competition and global competition and more and more search resources are guided into promising regions. Simulated annealing avoids entrapment in local minima. Two examples of multiple job scheduling were investigated. Results obtained with GESA were superior to those obtained with a simulated annealing approach described in prior literatures.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"411 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127599474","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}
{"title":"Recognizing patterns in protein sequences using iteration-performing calculations in genetic programming","authors":"J. Koza","doi":"10.1109/ICEC.1994.350008","DOIUrl":"https://doi.org/10.1109/ICEC.1994.350008","url":null,"abstract":"Uses genetic programming with automatically defined functions (ADFs) for the dynamic creation of a pattern-recognizing computer program consisting of initially-unknown detectors, an initially-unknown iterative calculation incorporating the as-yet-undiscovered detectors, and an initially-unspecified final calculation incorporating the results of the as-yet-unspecified iteration. The program's goal is to recognize a given protein segment as being a transmembrane domain or non-transmembrane area of the protein. Genetic programming with automatic function definition is given a training set of differently-sized mouse protein segments and their correct classification. Correlation is used as the fitness measure. Automatic function definition enables genetic programming to dynamically create subroutines (detectors). A restricted form of iteration is introduced to enable genetic programming to perform calculations on the values returned by the detectors. When cross-validated, the best genetically-evolved recognizer for transmembrane domains achieves an out-of-sample correlation of 0.968 and an out-of-sample error rate of 1.6%. This error rate is better than that recently reported for five other methods.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"2015 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121006641","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}
{"title":"A genetic algorithm for floorplan area optimization","authors":"M. Reorda, M. Rebaudengo","doi":"10.1109/ICEC.1994.350035","DOIUrl":"https://doi.org/10.1109/ICEC.1994.350035","url":null,"abstract":"The paper deals with the problem of floorplan area optimization; an approach based on genetic algorithms is proposed. The method produces optimal results with CPU time requirements comparable with the ones of other approaches but presents some advantages: it requires a limited amount of memory to store partial results, it is not sensible to special structures like nested wheels, it allows additional constraints to be easily taken into account, it allows the user to easily trade off CPU time with result accuracy, it is simple to implement. Experimental results on the biggest problems proposed in the literature are reported.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121207299","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}
{"title":"A combined neural and genetic learning algorithm","authors":"L. Tsinas, B. Dachwald","doi":"10.1109/ICEC.1994.349968","DOIUrl":"https://doi.org/10.1109/ICEC.1994.349968","url":null,"abstract":"Neural networks and genetic algorithms are well-known representatives of learning procedures. In this paper a hybrid procedure, which combines both concepts, is introduced. Its functionality is presented on a typical pattern recognition problem.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121365883","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}