Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/18818
Title: Ensemble of mutation strategies in evolutionary programming
Authors: Mallipeddi Sireehsa.
Keywords: DRNTU::Engineering::Computer science and engineering::Theory of computation
Issue Date: 2008
Abstract: Evolutionary programming (EP) has been applied to solve many numerical global optimization problems successfully during the past four decades. The ability of the algorithm to seach for global optimum depends on the mutation strategy adapted. During the last four decades several mutation operators such as Gaussian and Cauchy (a special case of Levy’s mutation) have been used with EP. According to no free lunch theorem, it is impossible for EP with a single mutation operator to outperform on every problem. For example, Classical EP (CEP) that uses Gaussian mutation is better at search in a local neighborhood while Fast EP (FEP) that uses Cauchy mutation is very good in a large neighborhood. This may give an impression that CEP is better in unimodal problems while FEP is better on multimodal problems/ but in a particular problem due to lack of proper knowledge wbout the global optimum point we need to have global exploration along with local exploiting during every generation of the evolution process. Motivated by these observations, we propose and Ensemble of mutation operator (Gaussian and Cauchy) to solve CEC 2005 problems by using evolutionary programming (EP) algorithm. This eliminates the need to perform the trail-and-error search for the best mutation operator, but also enable us to benefit from the match between the exploration-exploitation stages of the search process. The distinguished feature of Ensemble algorithm is the effective usage of every function call. The offspring population produced by a particular mutation operator may dominate the others at particular stage of the optimization process. Furthermore, an offspring produced by a particular mutation operator may be rejected by its own population, but could be accepted by the population of other mutation method. We employ two different mutation operators (Gaussian and Cauchy) present in the literature to form the ensemble.
URI: http://hdl.handle.net/10356/18818
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
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