Preserving and Exploiting Genetic Diversity in Evolutionary Programming Algorithms

• 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
• ISSN: 1089-778X; 1941-0026
• DOI: 10.1109/TEVC.2008.2011742

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.