Global Convergence Analysis of Non-Crossover Genetic Algorithm and Its Application to Optimization
Selection, crossover, and mutation are three main operators of the canonical genetic algorithm (CGA). This paper presents a new approach to the genetic algorithm. This new approach applies only to mutation and selection operators. The paper proves that the search process of the non-crossover genetic...
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| Veröffentlicht in: | Journal of systems engineering and electronics 2002, Vol.13 (2), p.84-91 |
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| creator | Dai Xiaoming Sun Rong Zou Runmin Xu Chao Shao Huihe |
| description | Selection, crossover, and mutation are three main operators of the canonical genetic algorithm (CGA). This paper presents a new approach to the genetic algorithm. This new approach applies only to mutation and selection operators. The paper proves that the search process of the non-crossover genetic algorithm (NCGA) is an ergodic homogeneous Markov chain. The proof of its convergence to global optimum is presented. Some nonlinear multi-modal optimization problems are applied to test the efficacy of the NCGA. NP-hard traveling salesman problem (TSP) is cited here as the benchmark problem to test the efficiency of the algorithm. The simulation result shows that NCGA achieves much faster convergence speed than CGA in terms of CPU time. The convergence speed per epoch of NCGA is also faster than that of CGA. |
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This paper presents a new approach to the genetic algorithm. This new approach applies only to mutation and selection operators. The paper proves that the search process of the non-crossover genetic algorithm (NCGA) is an ergodic homogeneous Markov chain. The proof of its convergence to global optimum is presented. Some nonlinear multi-modal optimization problems are applied to test the efficacy of the NCGA. NP-hard traveling salesman problem (TSP) is cited here as the benchmark problem to test the efficiency of the algorithm. The simulation result shows that NCGA achieves much faster convergence speed than CGA in terms of CPU time. The convergence speed per epoch of NCGA is also faster than that of CGA.</description><identifier>ISSN: 1004-4132</identifier><language>eng</language><publisher>Dept. of Auto., School of Electric and Information, Shanghai Jiaotong University, Shanghai 200030, P. R. 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All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttp://image.cqvip.com/vip1000/qk/85918X/85918X.jpg</thumbnail><link.rule.ids>314,777,781,4010</link.rule.ids></links><search><creatorcontrib>Dai Xiaoming Sun Rong Zou Runmin Xu Chao Shao Huihe</creatorcontrib><title>Global Convergence Analysis of Non-Crossover Genetic Algorithm and Its Application to Optimization</title><title>Journal of systems engineering and electronics</title><addtitle>Journal of Systems Engineering and Electronics</addtitle><description>Selection, crossover, and mutation are three main operators of the canonical genetic algorithm (CGA). This paper presents a new approach to the genetic algorithm. This new approach applies only to mutation and selection operators. The paper proves that the search process of the non-crossover genetic algorithm (NCGA) is an ergodic homogeneous Markov chain. The proof of its convergence to global optimum is presented. Some nonlinear multi-modal optimization problems are applied to test the efficacy of the NCGA. NP-hard traveling salesman problem (TSP) is cited here as the benchmark problem to test the efficiency of the algorithm. The simulation result shows that NCGA achieves much faster convergence speed than CGA in terms of CPU time. The convergence speed per epoch of NCGA is also faster than that of CGA.</description><subject>algorithm</subject><subject>Canonical</subject><subject>chain</subject><subject>convergence</subject><subject>Ergodic</subject><subject>Genetic</subject><subject>Global</subject><subject>homogeneous</subject><subject>Markov</subject><issn>1004-4132</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2002</creationdate><recordtype>article</recordtype><recordid>eNotUMtOwzAQzAEkqtJ_8IEjkfxI7OYYRVAqVfTSe-Rn6uDaITaP9usxlF1pR6OZHWn3plggCKuyQgTfFasYR_hbDGIMF4XYuCC4A13wn3oetJcatJ67c7QRBANegy-7OcQYsgw22utkJWjdEGabjifAvQLbFEE7Tc5KnmzwIAWwn5I92csfvy9uDXdRr_5xWRyenw7dS7nbb7ZduyslpVUeQiGJZcMYo0JSqemaKa2UIaiqIKwlFoIxZRinTBhNGigENAbWjGPFEVkWj9fYL-4N90M_ho85XxL77zTIs7qMsdcYQpwbVdn-cLXLY_DDu80Lgss3Y53u878QWdeENuQHRbthfw</recordid><startdate>2002</startdate><enddate>2002</enddate><creator>Dai Xiaoming Sun Rong Zou Runmin Xu Chao Shao Huihe</creator><general>Dept. of Auto., School of Electric and Information, Shanghai Jiaotong University, Shanghai 200030, P. 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This paper presents a new approach to the genetic algorithm. This new approach applies only to mutation and selection operators. The paper proves that the search process of the non-crossover genetic algorithm (NCGA) is an ergodic homogeneous Markov chain. The proof of its convergence to global optimum is presented. Some nonlinear multi-modal optimization problems are applied to test the efficacy of the NCGA. NP-hard traveling salesman problem (TSP) is cited here as the benchmark problem to test the efficiency of the algorithm. The simulation result shows that NCGA achieves much faster convergence speed than CGA in terms of CPU time. The convergence speed per epoch of NCGA is also faster than that of CGA.</abstract><pub>Dept. of Auto., School of Electric and Information, Shanghai Jiaotong University, Shanghai 200030, P. R. China%College of Information Science and Enginereing, Central South University, Changsha 410083, P. R. China</pub><tpages>8</tpages></addata></record> |
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| ispartof | Journal of systems engineering and electronics, 2002, Vol.13 (2), p.84-91 |
| issn | 1004-4132 |
| language | eng |
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| source | IEEE Power & Energy Library; EZB-FREE-00999 freely available EZB journals |
| subjects | algorithm Canonical chain convergence Ergodic Genetic Global homogeneous Markov |
| title | Global Convergence Analysis of Non-Crossover Genetic Algorithm and Its Application to Optimization |
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