Efficient Use of Variation in Evolutionary Optimization
Evolutionary algorithms face a fundamental trade-off between exploration and exploitation. Rapid performance improvement tends to be accompanied by a rapid loss of diversity from the population of potential solutions, causing premature convergence on local rather than global optima. However, the rat...
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| Format: | Article |
| Language: | English |
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Wiley
2010-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2010/696345 |
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| author | John W. Pepper |
| author_facet | John W. Pepper |
| author_sort | John W. Pepper |
| collection | DOAJ |
| description | Evolutionary algorithms face a fundamental trade-off between exploration and exploitation. Rapid performance improvement tends to be accompanied by a rapid loss of diversity from the population of potential solutions, causing premature convergence on local rather than global optima. However, the rate at which diversity is lost from a population is not simply a function of the strength of selection but also its efficiency, or rate of performance improvement relative to loss of variation. Selection efficiency can be quantified as the linear correlation between objective performance and reproduction. Commonly used selection algorithms contain several sources of inefficiency, some of which are easily avoided and others of which are not. Selection algorithms based on continuously varying generation time instead of discretely varying number of offspring can approach the theoretical limit on the efficient use of population diversity. |
| format | Article |
| id | doaj-art-a074442bacee4832a9465e971b7e72f9 |
| institution | OA Journals |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2010-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-a074442bacee4832a9465e971b7e72f92025-08-20T02:20:52ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322010-01-01201010.1155/2010/696345696345Efficient Use of Variation in Evolutionary OptimizationJohn W. Pepper0Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe, NM 87501, USAEvolutionary algorithms face a fundamental trade-off between exploration and exploitation. Rapid performance improvement tends to be accompanied by a rapid loss of diversity from the population of potential solutions, causing premature convergence on local rather than global optima. However, the rate at which diversity is lost from a population is not simply a function of the strength of selection but also its efficiency, or rate of performance improvement relative to loss of variation. Selection efficiency can be quantified as the linear correlation between objective performance and reproduction. Commonly used selection algorithms contain several sources of inefficiency, some of which are easily avoided and others of which are not. Selection algorithms based on continuously varying generation time instead of discretely varying number of offspring can approach the theoretical limit on the efficient use of population diversity.http://dx.doi.org/10.1155/2010/696345 |
| spellingShingle | John W. Pepper Efficient Use of Variation in Evolutionary Optimization Applied Computational Intelligence and Soft Computing |
| title | Efficient Use of Variation in Evolutionary Optimization |
| title_full | Efficient Use of Variation in Evolutionary Optimization |
| title_fullStr | Efficient Use of Variation in Evolutionary Optimization |
| title_full_unstemmed | Efficient Use of Variation in Evolutionary Optimization |
| title_short | Efficient Use of Variation in Evolutionary Optimization |
| title_sort | efficient use of variation in evolutionary optimization |
| url | http://dx.doi.org/10.1155/2010/696345 |
| work_keys_str_mv | AT johnwpepper efficientuseofvariationinevolutionaryoptimization |