Introduction to the Special Issue: Self-Adaptation

TLDR: Today, it is widely accepted in the evolutionary computation community that the principle of self-adaptation of strategy parameters, as proposed by Schwefel (1992) is one of the most sophisticated methods to tackle the problem of adjusting the control parameters of an evolutionary algorithm during the course of the optimization process.

Abstract: Today, it is widely accepted in the evolutionary computation community that the principle of self-adaptation of strategy parameters, as proposed by Schwefel (1992) is one of the most sophisticated methods to tackle the problem of adjusting the control parameters (e.g., mutation rates or mutation step sizes) of an evolutionary algorithm during the course of the optimization process. Essentially, the distinguishing feature of self-adaptive parameter control mechanisms is that the control parameters (also called strategy parameters) are evolved by the evolutionary algorithm, rather than exogenously defined or modified according to some fixed schedule. Following classifications offered by Angeline (1995) and Hinterding et al. (1997), the existing approaches for strategy parameter control (as opposed to static parameter settings, i.e., using no control at all) in evolutionary algorithms can be classified as follows: