Crossover versus Mutation: A Comparative Analysis of the Evolutionary Strategy of Genetic Algorithms Applied to Combinatorial Optimization Problems
Since their first formulation, genetic algorithms (GAs) have been one of the most widely used techniques to solve combinatorial optimization problems. The basic structure of the GAs is known by the scientific community, and thanks to their easy application and good performance, GAs are the focus of...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/154676 |
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| author | E. Osaba R. Carballedo F. Diaz E. Onieva I. de la Iglesia A. Perallos |
| author_facet | E. Osaba R. Carballedo F. Diaz E. Onieva I. de la Iglesia A. Perallos |
| author_sort | E. Osaba |
| collection | DOAJ |
| description | Since their first formulation, genetic algorithms (GAs) have been one of the most widely used techniques to solve combinatorial optimization problems. The basic structure of the GAs is known by the scientific community, and thanks to their easy application and good performance, GAs are the focus of a lot of research works annually. Although throughout history there have been many studies analyzing various concepts of GAs, in the literature there are few studies that analyze objectively the influence of using blind crossover operators for combinatorial optimization problems. For this reason, in this paper a deep study on the influence of using them is conducted. The study is based on a comparison of nine techniques applied to four well-known combinatorial optimization problems. Six of the techniques are GAs with different configurations, and the remaining three are evolutionary algorithms that focus exclusively on the mutation process. Finally, to perform a reliable comparison of these results, a statistical study of them is made, performing the normal distribution z-test. |
| format | Article |
| id | doaj-art-0530ae233a0a4beeb52ea9ae662a1944 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-0530ae233a0a4beeb52ea9ae662a19442025-02-03T01:31:44ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/154676154676Crossover versus Mutation: A Comparative Analysis of the Evolutionary Strategy of Genetic Algorithms Applied to Combinatorial Optimization ProblemsE. Osaba0R. Carballedo1F. Diaz2E. Onieva3I. de la Iglesia4A. Perallos5Deusto Institute of Technology (DeustoTech), University of Deusto, Avenue Universidades 24, 48007 Bilbao, SpainDeusto Institute of Technology (DeustoTech), University of Deusto, Avenue Universidades 24, 48007 Bilbao, SpainDeusto Institute of Technology (DeustoTech), University of Deusto, Avenue Universidades 24, 48007 Bilbao, SpainDeusto Institute of Technology (DeustoTech), University of Deusto, Avenue Universidades 24, 48007 Bilbao, SpainDeusto Institute of Technology (DeustoTech), University of Deusto, Avenue Universidades 24, 48007 Bilbao, SpainDeusto Institute of Technology (DeustoTech), University of Deusto, Avenue Universidades 24, 48007 Bilbao, SpainSince their first formulation, genetic algorithms (GAs) have been one of the most widely used techniques to solve combinatorial optimization problems. The basic structure of the GAs is known by the scientific community, and thanks to their easy application and good performance, GAs are the focus of a lot of research works annually. Although throughout history there have been many studies analyzing various concepts of GAs, in the literature there are few studies that analyze objectively the influence of using blind crossover operators for combinatorial optimization problems. For this reason, in this paper a deep study on the influence of using them is conducted. The study is based on a comparison of nine techniques applied to four well-known combinatorial optimization problems. Six of the techniques are GAs with different configurations, and the remaining three are evolutionary algorithms that focus exclusively on the mutation process. Finally, to perform a reliable comparison of these results, a statistical study of them is made, performing the normal distribution z-test.http://dx.doi.org/10.1155/2014/154676 |
| spellingShingle | E. Osaba R. Carballedo F. Diaz E. Onieva I. de la Iglesia A. Perallos Crossover versus Mutation: A Comparative Analysis of the Evolutionary Strategy of Genetic Algorithms Applied to Combinatorial Optimization Problems The Scientific World Journal |
| title | Crossover versus Mutation: A Comparative Analysis of the Evolutionary Strategy of Genetic Algorithms Applied to Combinatorial Optimization Problems |
| title_full | Crossover versus Mutation: A Comparative Analysis of the Evolutionary Strategy of Genetic Algorithms Applied to Combinatorial Optimization Problems |
| title_fullStr | Crossover versus Mutation: A Comparative Analysis of the Evolutionary Strategy of Genetic Algorithms Applied to Combinatorial Optimization Problems |
| title_full_unstemmed | Crossover versus Mutation: A Comparative Analysis of the Evolutionary Strategy of Genetic Algorithms Applied to Combinatorial Optimization Problems |
| title_short | Crossover versus Mutation: A Comparative Analysis of the Evolutionary Strategy of Genetic Algorithms Applied to Combinatorial Optimization Problems |
| title_sort | crossover versus mutation a comparative analysis of the evolutionary strategy of genetic algorithms applied to combinatorial optimization problems |
| url | http://dx.doi.org/10.1155/2014/154676 |
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