Preserving and Exploiting Genetic Diversity in Evolutionary Programming Algorithms

Evolution programming (EP) is an important category of evolutionary algorithms. It relies primarily on mutation operators to search for solutions of function optimization problems (FOPs). Recently a series of new mutation operators have been proposed in order to improve the performance of EP. One pr...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation 2009-06, Vol.13 (3), p.661-673
Main Authors: Chen, Gang, Low, Chor Ping, Yang, Zhonghua
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial intelligence
Computational modeling
Computer science
control theory
systems
Data structures
Evolutionary algorithms
Evolutionary computation
Evolutionary optimization
evolutionary programming (EP)
Exact sciences and technology
Gaussian distribution
Genetic mutations
Genetic programming
Genetics
Mathematical analysis
Mathematical models
Mutation
Mutations
Operators
Performance enhancement
Probability distribution
Random number generation
selection strategy
Studies
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Summary:Evolution programming (EP) is an important category of evolutionary algorithms. It relies primarily on mutation operators to search for solutions of function optimization problems (FOPs). Recently a series of new mutation operators have been proposed in order to improve the performance of EP. One prominent example is the fast EP (FEP) algorithm which employs a mutation operator based on the Cauchy distribution instead of the commonly used Gaussian distribution. In this paper, we seek to improve the performance of EP via exploring another important factor of EP, namely, the selection strategy. Three selection rules R1-R3 have been presented to encourage both fitness diversity and solution diversity. Meanwhile, two solution exchange rules R4 and R5 have been introduced to further exploit the preserved genetic diversity. Simple theoretical analysis suggests that through the proper use of R1-R5, EP is more likely to find high-fitness solutions quickly. Our claim has been examined on 25 benchmark functions. Empirical evidence shows that our solution selection and exchange rules can significantly enhance the performance of EP.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2008.2011742