A DNA Based Evolutionary Algorithm for the Minimal Set Cover Problem
With the birth of DNA computing, Paun et al. proposed an elegant algorithm to this problem based on the sticky model proposed by Roweis. However, the drawback of this algorithm is that the “exponential curse” is hard to overcome, and therefore its application to large instance is limited. In this s...
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| creator | Liu, Wenbin Zhu, Xiangou Xu, Guandong Zhang, Qiang Gao, Lin |
| description | With the birth of DNA computing, Paun et al. proposed an elegant algorithm to this problem based on the sticky model proposed by Roweis. However, the drawback of this algorithm is that the “exponential curse” is hard to overcome, and therefore its application to large instance is limited. In this s paper, we present a DNA based evolutionary algorithm to solve this problem, which takes advantage of both the massive parallelism and the evolution strategy by traditional EAs. The fitness of individuals is defined as the negative value of their length. Both the crossover and mutation can be implemented in a reshuffle process respectively. We also present a short discussion about population size, mutation probability, crossover probability, and genetic operations over multiple points. In the end, we also present some problems needed to be further considered in the future. |
| doi_str_mv | 10.1007/11538356_9 |
| format | Conference Proceeding |
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However, the drawback of this algorithm is that the “exponential curse” is hard to overcome, and therefore its application to large instance is limited. In this s paper, we present a DNA based evolutionary algorithm to solve this problem, which takes advantage of both the massive parallelism and the evolution strategy by traditional EAs. The fitness of individuals is defined as the negative value of their length. Both the crossover and mutation can be implemented in a reshuffle process respectively. We also present a short discussion about population size, mutation probability, crossover probability, and genetic operations over multiple points. 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However, the drawback of this algorithm is that the “exponential curse” is hard to overcome, and therefore its application to large instance is limited. In this s paper, we present a DNA based evolutionary algorithm to solve this problem, which takes advantage of both the massive parallelism and the evolution strategy by traditional EAs. The fitness of individuals is defined as the negative value of their length. Both the crossover and mutation can be implemented in a reshuffle process respectively. We also present a short discussion about population size, mutation probability, crossover probability, and genetic operations over multiple points. In the end, we also present some problems needed to be further considered in the future.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Crossover Probability</subject><subject>Evolutionary Algorithm</subject><subject>Exact sciences and technology</subject><subject>Genetic Operation</subject><subject>Massive Parallelism</subject><subject>Peptide Nucleic Acid</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>3540282270</isbn><isbn>9783540282273</isbn><isbn>3540282262</isbn><isbn>9783540282266</isbn><isbn>9783540319078</isbn><isbn>3540319077</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2005</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNpFkMtOwzAURM1Loi3d8AXeILEJ3HvtxPEytOUhlYcErCMntdtAGldOqNS_J1WRWM1IczQaDWOXCDcIoG4RY5GKOMn1ERtr1VsJAjWo9JgNMEGMhJD6hA33AaVECk7ZAARQpJUU52zYtl8AQErTgE0zPn3J-J1p7YLPtr7-6SrfmLDjWb30oepWa-584N3K8ueqqdam5u-24xO_tYG_BV_Udn3BzpypWzv-0xH7vJ99TB6j-evD0ySbRxvCtIsotqaURlqXFmW_HBY2KeJFAWWhBaIShETCOdLaOkglISBJZ0gVkMgExIhdHXo3pi1N7YJpyqrNN6GfFXY5KtAxoei56wPX9lGztCEvvP9uc4R8f2H-f6H4BY4wW-o</recordid><startdate>2005</startdate><enddate>2005</enddate><creator>Liu, Wenbin</creator><creator>Zhu, Xiangou</creator><creator>Xu, Guandong</creator><creator>Zhang, Qiang</creator><creator>Gao, Lin</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2005</creationdate><title>A DNA Based Evolutionary Algorithm for the Minimal Set Cover Problem</title><author>Liu, Wenbin ; Zhu, Xiangou ; Xu, Guandong ; Zhang, Qiang ; Gao, Lin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p218t-25eac4a4ef8bc9780de6b5db0cb93117321223ff299ef084210124fa27b064603</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Crossover Probability</topic><topic>Evolutionary Algorithm</topic><topic>Exact sciences and technology</topic><topic>Genetic Operation</topic><topic>Massive Parallelism</topic><topic>Peptide Nucleic Acid</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Wenbin</creatorcontrib><creatorcontrib>Zhu, Xiangou</creatorcontrib><creatorcontrib>Xu, Guandong</creatorcontrib><creatorcontrib>Zhang, Qiang</creatorcontrib><creatorcontrib>Gao, Lin</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Wenbin</au><au>Zhu, Xiangou</au><au>Xu, Guandong</au><au>Zhang, Qiang</au><au>Gao, Lin</au><au>Zhang, Xiao-Ping</au><au>Huang, Guang-Bin</au><au>Huang, De-Shuang</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A DNA Based Evolutionary Algorithm for the Minimal Set Cover Problem</atitle><btitle>Advances in Intelligent Computing</btitle><date>2005</date><risdate>2005</risdate><spage>80</spage><epage>89</epage><pages>80-89</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>3540282270</isbn><isbn>9783540282273</isbn><isbn>3540282262</isbn><isbn>9783540282266</isbn><eisbn>9783540319078</eisbn><eisbn>3540319077</eisbn><abstract>With the birth of DNA computing, Paun et al. proposed an elegant algorithm to this problem based on the sticky model proposed by Roweis. However, the drawback of this algorithm is that the “exponential curse” is hard to overcome, and therefore its application to large instance is limited. In this s paper, we present a DNA based evolutionary algorithm to solve this problem, which takes advantage of both the massive parallelism and the evolution strategy by traditional EAs. The fitness of individuals is defined as the negative value of their length. Both the crossover and mutation can be implemented in a reshuffle process respectively. We also present a short discussion about population size, mutation probability, crossover probability, and genetic operations over multiple points. In the end, we also present some problems needed to be further considered in the future.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11538356_9</doi><tpages>10</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Advances in Intelligent Computing, 2005, p.80-89 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_17095213 |
| source | Springer Books |
| subjects | Applied sciences Artificial intelligence Computer science control theory systems Crossover Probability Evolutionary Algorithm Exact sciences and technology Genetic Operation Massive Parallelism Peptide Nucleic Acid |
| title | A DNA Based Evolutionary Algorithm for the Minimal Set Cover Problem |
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