Abstract
Genetic operators are primarily search operators in Evolution Strategies (ES). In fact, there are two important issues in the evolution process of the genetic search: exploration and exploitation. The analysis of the impact of the genetic operators in ES shows that the Classical Evolution Strategies (CES) relies on Gaussian mutation, whereas Fast Evolution Strategies (FES) selects Cauchy distribution as the primary mutation operator. With the analysis of the basic genetic operators of ES as well as their performances on a number of benchmark problems, this paper proposes an Improved Fast ES (IFES) which applies the search direction of global optimization into mutation operation to guide evolution process convergence, thus making the process quicker.
Extensive empirical studies have been carried out to evaluate the performances of IFES, FES and CES. The experimental results obtained from four widely used test functions show that IFES outperforms both FES and CES. It is therefore concluded that it is important to strike a balance between exploration and exploitation.
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Lin, G., Lu, X., Kang, L. (2009). Search Direction Made Evolution Strategies Faster. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2009. Communications in Computer and Information Science, vol 51. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04962-0_17
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DOI: https://doi.org/10.1007/978-3-642-04962-0_17
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