Multi-Subexpression Programming

Gene Expression Programming is a new and adaptive brand evolution algorithm which is developed on the basis of genetic algorithm. In recent years, Multi-Expression Programming which is proposed in the genetic programming is a linear structure coding scheme,its main feature is a chromosome contains m...

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Vydáno v:Applied Mechanics and Materials Ročník 411-414; s. 2067 - 2073
Hlavní autoři: Chen, Long Bin, He, Pei
Médium: Journal Article
Jazyk:angličtina
Vydáno: Zurich Trans Tech Publications Ltd 01.09.2013
Témata:
Average Fitness Value
MEP
GEP
Success Rate
Subexpression
Evolutionary Efficiency
ISBN:3037858648, 9783037858646
ISSN:1660-9336, 1662-7482, 1662-7482
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Abstract Gene Expression Programming is a new and adaptive brand evolution algorithm which is developed on the basis of genetic algorithm. In recent years, Multi-Expression Programming which is proposed in the genetic programming is a linear structure coding scheme,its main feature is a chromosome contains multiple expressions. The idea of MEP is introduced into the GEP in this paper, so a single GEP gene contains multiple solutions to solve the problem.The new algorithm analyzes each gene in the GEP to extract relational subexpressions, then fitness evaluate certain subexpressions to choose the best fitness as individuals fitness, and carry on related genetic manipulation. Finally, the improved algorithm experiment with GEP and MEP, compare their mining the same functions ability,record average fitness value and success rate. The experiment results show that the improved algorithm has better evolutionary efficiency.
AbstractList Gene Expression Programming is a new and adaptive brand evolution algorithm which is developed on the basis of genetic algorithm. In recent years, Multi-Expression Programming which is proposed in the genetic programming is a linear structure coding scheme,its main feature is a chromosome contains multiple expressions. The idea of MEP is introduced into the GEP in this paper, so a single GEP gene contains multiple solutions to solve the problem.The new algorithm analyzes each gene in the GEP to extract relational subexpressions, then fitness evaluate certain subexpressions to choose the best fitness as individuals fitness, and carry on related genetic manipulation. Finally, the improved algorithm experiment with GEP and MEP, compare their mining the same functions ability,record average fitness value and success rate. The experiment results show that the improved algorithm has better evolutionary efficiency.
Author He, Pei
Chen, Long Bin
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  organization: Peking University : Key Laboratory of High Confidence Software Technologies, Inistry of Education
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Cites_doi 10.1007/0-387-28356-0_5
10.1109/eh.2004.1310814
10.1007/s11432-011-4411-8
10.1016/j.ins.2005.03.012
10.1109/cnmt.2009.5374777
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10.1007/s11432-011-4200-4
10.1109/cec.2010.5586478
10.1109/ccdc.2008.4597554
10.1142/s0218213009000111
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Keywords Average Fitness Value
MEP
GEP
Success Rate
Subexpression
Evolutionary Efficiency
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Notes Selected, peer reviewed papers from the 2013 2nd International Conference on Information Technology and Management Innovation (ICITMI 2013), July 23-24, 2013, Zhuhai, China
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Snippet Gene Expression Programming is a new and adaptive brand evolution algorithm which is developed on the basis of genetic algorithm. In recent years,...
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