Evolutionary programming using mutations based on the Levy probability distribution
Studies evolutionary programming with mutations based on the Levy probability distribution. The Levy probability distribution has an infinite second moment and is, therefore, more likely to generate an offspring that is farther away from its parent than the commonly employed Gaussian mutation. Such...
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| Veröffentlicht in: | IEEE transactions on evolutionary computation 2004-02, Vol.8 (1), p.1-13 |
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| container_title | IEEE transactions on evolutionary computation |
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| creator | Lee, C.-Y. Yao, X. |
| description | Studies evolutionary programming with mutations based on the Levy probability distribution. The Levy probability distribution has an infinite second moment and is, therefore, more likely to generate an offspring that is farther away from its parent than the commonly employed Gaussian mutation. Such likelihood depends on a parameter /spl alpha/ in the Levy distribution. We propose an evolutionary programming algorithm using adaptive as well as nonadaptive Levy mutations. The proposed algorithm was applied to multivariate functional optimization. Empirical evidence shows that, in the case of functions having many local optima, the performance of the proposed algorithm was better than that of classical evolutionary programming using Gaussian mutation. |
| doi_str_mv | 10.1109/TEVC.2003.816583 |
| format | Article |
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The Levy probability distribution has an infinite second moment and is, therefore, more likely to generate an offspring that is farther away from its parent than the commonly employed Gaussian mutation. Such likelihood depends on a parameter /spl alpha/ in the Levy distribution. We propose an evolutionary programming algorithm using adaptive as well as nonadaptive Levy mutations. The proposed algorithm was applied to multivariate functional optimization. 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The Levy probability distribution has an infinite second moment and is, therefore, more likely to generate an offspring that is farther away from its parent than the commonly employed Gaussian mutation. Such likelihood depends on a parameter /spl alpha/ in the Levy distribution. We propose an evolutionary programming algorithm using adaptive as well as nonadaptive Levy mutations. The proposed algorithm was applied to multivariate functional optimization. Empirical evidence shows that, in the case of functions having many local optima, the performance of the proposed algorithm was better than that of classical evolutionary programming using Gaussian mutation.</description><subject>Adaptive algorithms</subject><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Biology computing</subject><subject>Computer science; control theory; systems</subject><subject>Connectionism. Neural networks</subject><subject>Electronic switching systems</subject><subject>Evolution (biology)</subject><subject>Evolutionary algorithms</subject><subject>Evolutionary computation</subject><subject>Exact sciences and technology</subject><subject>Fractals</subject><subject>Functional programming</subject><subject>Gaussian</subject><subject>Genetic algorithms</subject><subject>Genetic mutations</subject><subject>Genetic programming</subject><subject>Mathematical analysis</subject><subject>Mutations</subject><subject>Optimization</subject><subject>Parents</subject><subject>Probability distribution</subject><issn>1089-778X</issn><issn>1941-0026</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2004</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNp9kctrHDEMxofSQNOk90IuQ6DpabbyY2zrWJbtAxZyaFp6M_aMJnGYR2LPBPLf15MNBHrIRRLopw9JX1F8ZLBhDPDL1e7PdsMBxMYwVRvxpjhmKFkFwNXbXIPBSmvz913xPqVbACZrhsfFr93D1C9zmEYXH8u7OF1HNwxhvC6XtMZhmd3aTaV3idpyGsv5hso9PTzR3vnQh_mxbEOaY_BPSqfFUef6RB-e80nx-9vuavuj2l9-_7n9uq8aiXKuGGEDHo2khpGWLRAq9NJ7waRDp4F1pDpCBC26VhnkvlWslcQ7Y3SjxUnx-aCbF7lfKM12CKmhvncjTUuyBhWXolZ1Ji9eJbnRgiNnGTz_D7ydljjmK6wxArXMD84QHKAmTilF6uxdDEP-n2VgVzPsaoZdzbAHM_LIp2ddlxrXd9GNTUgvc7VEFAoyd3bgAhG9tLlSQoP4B-5Ck24</recordid><startdate>20040201</startdate><enddate>20040201</enddate><creator>Lee, C.-Y.</creator><creator>Yao, X.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Neural networks</topic><topic>Electronic switching systems</topic><topic>Evolution (biology)</topic><topic>Evolutionary algorithms</topic><topic>Evolutionary computation</topic><topic>Exact sciences and technology</topic><topic>Fractals</topic><topic>Functional programming</topic><topic>Gaussian</topic><topic>Genetic algorithms</topic><topic>Genetic mutations</topic><topic>Genetic programming</topic><topic>Mathematical analysis</topic><topic>Mutations</topic><topic>Optimization</topic><topic>Parents</topic><topic>Probability distribution</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lee, C.-Y.</creatorcontrib><creatorcontrib>Yao, X.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>IEEE transactions on evolutionary computation</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Lee, C.-Y.</au><au>Yao, X.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evolutionary programming using mutations based on the Levy probability distribution</atitle><jtitle>IEEE transactions on evolutionary computation</jtitle><stitle>TEVC</stitle><date>2004-02-01</date><risdate>2004</risdate><volume>8</volume><issue>1</issue><spage>1</spage><epage>13</epage><pages>1-13</pages><issn>1089-778X</issn><eissn>1941-0026</eissn><coden>ITEVF5</coden><abstract>Studies evolutionary programming with mutations based on the Levy probability distribution. The Levy probability distribution has an infinite second moment and is, therefore, more likely to generate an offspring that is farther away from its parent than the commonly employed Gaussian mutation. Such likelihood depends on a parameter /spl alpha/ in the Levy distribution. We propose an evolutionary programming algorithm using adaptive as well as nonadaptive Levy mutations. The proposed algorithm was applied to multivariate functional optimization. Empirical evidence shows that, in the case of functions having many local optima, the performance of the proposed algorithm was better than that of classical evolutionary programming using Gaussian mutation.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/TEVC.2003.816583</doi><tpages>13</tpages><oa>free_for_read</oa></addata></record> |
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| ispartof | IEEE transactions on evolutionary computation, 2004-02, Vol.8 (1), p.1-13 |
| issn | 1089-778X 1941-0026 |
| language | eng |
| recordid | cdi_ieee_primary_1266370 |
| source | IEEE Electronic Library (IEL) |
| subjects | Adaptive algorithms Algorithms Applied sciences Artificial intelligence Biology computing Computer science control theory systems Connectionism. Neural networks Electronic switching systems Evolution (biology) Evolutionary algorithms Evolutionary computation Exact sciences and technology Fractals Functional programming Gaussian Genetic algorithms Genetic mutations Genetic programming Mathematical analysis Mutations Optimization Parents Probability distribution |
| title | Evolutionary programming using mutations based on the Levy probability distribution |
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