Toward a Theory of Evolution Strategies: Self-Adaptation
- 1 September 1995
- journal article
- Published by MIT Press in Evolutionary Computation
- Vol. 3 (3), 311-347
- https://doi.org/10.1162/evco.1995.3.3.311
Abstract
This paper analyzes the self-adaptation (SA) algorithm widely used to adapt strategy parameters of the evolution strategy (ES) in order to obtain maximal ES performance. The investigations are concentrated on the adaptation of one general mutation strength σ (called σSA) in (1, λ) ESs. The hypersphere serves as the fitness model. Starting from an introduction to the basic concept of self-adaptation, a framework for the analysis of σSA is developed on two levels: a microscopic level, concerning the description of the stochastic changes from one generation to the next, and a macroscopic level, describing the evolutionary dynamics of the σSA over time (generations). The σSA requires the fixing of a new strategy parameter, known as the learning parameter. The influence of this parameter on ES performance is investigated and rules for its tuning are presented and discussed. The results of the theoretical analysis are compared with ES experiments; it will be shown that applying Schwefel's τ-scaling rule guarantees the linear convergence order of the ES.Keywords
This publication has 1 reference indexed in Scilit:
- Toward a Theory of Evolution Strategies: Some Asymptotical Results from the (1,+ λ)-TheoryEvolutionary Computation, 1993