Abstract

Nature-inspired algorithms attract many researchers worldwide for solving the hardest optimization problems. One of the well-known members of this extensive family is the evolutionary strategy ES algorithm. To date, many variants of this algorithm have emerged for solving continuous as well as combinatorial problems. One of the more promising variants, a self-adaptive evolutionary algorithm, has recently been proposed that enables a self-adaptation of its control parameters. In this paper, we discuss and evaluate popular common and self-adaptive evolutionary strategy (ES) algorithms. In particular, we present an empirical comparison between three self-adaptive ES variants and common ES methods. In order to assure a fair comparison, we test the methods by using a number of well-known unimodal and multimodal, separable and non-separable, benchmark optimization problems for different dimensions and population size. The results of this experiments study were promising and have encouraged us to invest more efforts into developing in this direction.

Highlights

  • The most successful methods in global optimization are based on stochastic components, which allow escaping from local optima and overcome premature stagnation

  • The values of strategy parameters those modify the problem variables, which are the most adapted to the fitness landscape, as determined by the fitness function of the problem to be solved during the evolutionary search process

  • In order to better adapt the parameter setting to the fitness landscape of the problem, the self-adaptation of strategy parameters has been emerged that is tightly connected with a development of so named evolution strategies (SA-ES) [10,11,9]

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Summary

A Study on Self-adaptation in the Evolutionary Strategy Algorithm

To cite this version: Noureddine Boukhari, Fatima Debbat, Nicolas Monmarché, Mohamed Slimane. A Study on Selfadaptation in the Evolutionary Strategy Algorithm. 6th IFIP International Conference on Computational Intelligence and Its Applications (CIIA), May 2018, Oran, Algeria. HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. 1, 2 Department of Computer Science, Mascara University, Mascara, Algeria 3,4 Laboratoire Informatique (EA6300), Université François Rabelais Tours, 64, avenue Jean

Introduction
Evolution Strategies
Pseudo code For Evolutionary Algorithms
Mutation And Parameter Control
Deterministic parameter control
Adaptive parameter control
Self-Adaptation
Next Generation Selection Strategies
Mutation Operators
Uncorrelated Mutation with Individual Step size (type 2)
Correlated Mutation (type 3)
Numerical and Equations Experiments
Algorithm Parameters Used for Experiments
Behavior in Higher-Dimensional Search Spaces
Behaviors for Multi-Modal Objective Functions
Conclusion and Future Work

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