Silja Meyer-Nieberg

Self-Adaptation in Evolutionary Algorithms

Summary. In this chapter, we will give an overview over self-adaptive methods in evolutionary algorithms. Self-adaptation in its purest meaning is a state-of-the-art method to adjust the setting of control parameters. It is called self-adaptive because the algorithm controls the setting of these parameters itself – embedding them into an individual’s genome and evolving them. We will start with a short history of adaptation methods. The section is followed by a presentation of classification schemes for adaptation rules. Afterwards, we will review empirical and theoretical research of self-adaptation methods applied in genetic algorithms, evolutionary programming, and evolution strategies.

Self-adaptation of evolution strategies under noisy fitness evaluations

This paper investigates the self-adaptation behavior of (

Mutative self-adaptation on the sharp and parabolic ridge

1,lambda

Self-adaptation on the ridge function class: first results for the sharp ridge

)-evolution strategies (ES) on the noisy sphere model. To this end, the stochastic system dynamics is approximated on the level of the mean value dynamics. Being based on this “microscopic” analysis, the steady state behavior of the ES for the scaled noise scenario and the constant noise strength scenario will be theoretically analyzed and compared with real ES runs. An explanation will be given for the random walk like behavior of the mutation strength in the vicinity of the steady state. It will be shown that this is a peculiarity of the

Coordinating a team of searchers: of ants, swarms, and slime molds

(1,lambda)

The Dynamical Systems Approach - Progress Measures and Convergence Properties.

-ES and that intermediate recombination strategies do not suffer from such behavior.