A Theoretical Development and Analysis of Jumping Gene Genetic Algorithm

Recently, gene transpositions have gained their power and attentions in computational evolutionary algorithm designs. In 2004, the Jumping Gene Genetic Algorithm (JGGA) was first proposed and two new gene transposition operations, namely, cut-and-paste and copy-and-paste, were introduced. Although t...

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Published inIEEE transactions on industrial informatics Vol. 7; no. 3; pp. 408 - 418
Main Authors Kit Sang Tang, Yin, R. J., Sam Kwong, Kai Tat Ng, Man, K. F.
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.08.2011
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Biological cells
Computational modeling
Design engineering
Equilibrium
gene transposition
Genes
Genetic algorithms
Jumping
jumping genes
Mathematical model
Mathematical models
Optimization
Reproduction
schema
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Abstract Recently, gene transpositions have gained their power and attentions in computational evolutionary algorithm designs. In 2004, the Jumping Gene Genetic Algorithm (JGGA) was first proposed and two new gene transposition operations, namely, cut-and-paste and copy-and-paste, were introduced. Although the outperformance of JGGA has been demonstrated by some detailed statistical analyses based on numerical simulations, more rigorous theoretical justification is still in vain. In this paper, a mathematical model based on schema is derived. It then provides theoretical justifications on why JGGA is superiority in searching, particularly when it is applied to solve multiobjective optimization problems. The studies are also further verified by solving some optimization problems and comparisons are made between different optimization algorithms.
AbstractList Recently, gene transpositions have gained their power and attentions in computational evolutionary algorithm designs. In 2004, the Jumping Gene Genetic Algorithm (JGGA) was first proposed and two new gene transposition operations, namely, cut-and-paste and copy-and-paste, were introduced. Although the outperformance of JGGA has been demonstrated by some detailed statistical analyses based on numerical simulations, more rigorous theoretical justification is still in vain. In this paper, a mathematical model based on schema is derived. It then provides theoretical justifications on why JGGA is superiority in searching, particularly when it is applied to solve multiobjective optimization problems. The studies are also further verified by solving some optimization problems and comparisons are made between different optimization algorithms.
Author Kit Sang Tang
Man, K. F.
Yin, R. J.
Sam Kwong
Kai Tat Ng
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SubjectTerms Algorithms
Biological cells
Computational modeling
Design engineering
Equilibrium
gene transposition
Genes
Genetic algorithms
Jumping
jumping genes
Mathematical model
Mathematical models
Optimization
Reproduction
schema
Title A Theoretical Development and Analysis of Jumping Gene Genetic Algorithm
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