Mutation Strategy Based on Step Size and Survival Rate for Evolutionary Programming

Libin Hong,Jiadong Cui,Chenjian Liu,Fuchang Liu,Shi Cheng

doi:10.1155/2021/1336929

Libin Hong, Jiadong Cui + Show 3 more

Open Access

https://doi.org/10.1155/2021/1336929

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Abstract

Evolutionary programming (EP) uses a mutation as a unique operator. Gaussian, Cauchy, Lévy, and double exponential probability distributions and single-point mutation were nominated as mutation operators. Many mutation strategies have been proposed over the last two decades. The most recent EP variant was proposed using a step-size-based self-adaptive mutation operator. In SSEP, the mutation type with its parameters is selected based on the step size, which differs from generation to generation. Several principles for choosing proper parameters have been proposed; however, SSEP still has limitations and does not display outstanding performance on some benchmark functions. In this work, we proposed a novel mutation strategy based on both the “step size” and “survival rate” for EP (SSMSEP). SSMSEP-1 and SSMSEP-2 are two variants of SSMSEP, which use “survival rate” or “step size” separately. Our proposed method can select appropriate mutation operators and update parameters for mutation operators according to diverse landscapes during the evolutionary process. Compared with SSMSEP-1, SSMSEP-2, SSEP, and other EP variants, the SSMSEP demonstrates its robustness and stable performance on most benchmark functions tested.

Highlights

Evolutionary programming (EP) is a major evolutionary algorithm that attempts to find a global optimum for benchmark functions; mutation is the only available operator in EP
Over the last two decades, several mutation strategies have been adopted in the EP algorithm, such as combining mutation operators [4], which improved fast evolutionary programming (FEP) [5], mutation strategy called evolutionary programming (MSEP) [6], LMSEP [7], and Society has a self-adaptive mechanism (SSEP) [9]
E SSMSEP is inspired by the drawback of the SSEP

Summary

Introduction

Evolutionary programming (EP) is a major evolutionary algorithm that attempts to find a global optimum for benchmark functions; mutation is the only available operator in EP. Hong et al [6] proposed a mixed-mutation strategy called evolutionary programming (MSEP), wherein the appropriate mutation operator is selected during evolution based on the probabilities of the four mutation operators. We propose a novel mutation strategy that uses both “step size” and “survival rate” to control the selection of mutation operator/type for evolutionary programming (SSMSEP). E proposed mutation strategy conquers the loss of step size control on our tested benchmark functions. (7) Stop when the end condition is met; otherwise, k++ and go to Step 3 It is CEP [1], FEP [5], and LEP [2] when Mj is a random number generated by Gaussian, Cauchy, and Levy distributions, respectively.

Step Size and Survival Rate-Based Mutation Strategy

SSMSEP Implementation

Experimental Results

Conclusions and Summary