A Jumping Gene Paradigm for Evolutionary Multiobjective Optimization

A new evolutionary computing algorithm on the basis of the ldquojumping genesrdquo (JG) phenomenon is proposed in this paper. It emulates the gene transposition in the genome that was discovered by Nobel Laureate, Barbara McClintock, in her work on the corn plants. The principle of JGs that is adopt...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation 2008-04, Vol.12 (2), p.143-159
Main Authors: Chan, T.M., Man, K.F., Tang, K.S., Kwong, S.
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial intelligence
Bioinformatics
Biological cells
Classification algorithms
Computation
Computer science
control theory
systems
Convergence
Design optimization
Evolutionary
Evolutionary algorithms
Evolutionary computation
Exact sciences and technology
Genes
Genetic algorithms
Genetic algorithms (GAs)
Genomes
Genomics
jumping genes (JGs)
Learning and adaptive systems
multiobjective evolutionary algorithms (MOEAs)
Optimization
Robustness
Studies
test functions
Testing
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Summary:A new evolutionary computing algorithm on the basis of the ldquojumping genesrdquo (JG) phenomenon is proposed in this paper. It emulates the gene transposition in the genome that was discovered by Nobel Laureate, Barbara McClintock, in her work on the corn plants. The principle of JGs that is adopted for evolutionary computing is outlined. The procedures for executing the computational optimization are provided. A large number of constrained and unconstrained test functions have been utilized to verify this new scheme. Its performances on convergence and diversity have been statistically examined and comparisons with other evolutionary algorithms are carried out. It has been discovered that this new scheme is robust and able to provide outcomes quickly and accurately. A stringent measure of binary-indicator is also applied for algorithm classification. The outcome from this test indicates that the JG paradigm is a very competitive scheme for multiobjective optimization and also a compatible evolutionary computing scheme when speed in convergence, diversity, and accuracy are simultaneously required.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2007.895269