Strategy Equilibrium of Evolutionary Computation: Towards Its Algorithmic Mechanism Design

Abstract

We consider algorithmic design, enhancement, and improvement of evolutionary computation (EC) as a mechanism design problem. All individuals or several groups of individuals can be considered as self-interested agents. The individuals in EC can manipulate the parameter settings and operations of an EC algorithm by satisfying their own preferences rather than by following a fixed algorithm rule. EC algorithm designers or EC self-adaptive methods should construct appropriate rules and mechanisms for all agents (individuals) to conduct their evolution behavior correctly in order to definitely achieve the desired and pre-set objective(s) definitively. We propose a formal framework on parameter setting, strategy selection, and algorithmic design of EC by considering the strategy equilibrium implementation of a mechanism design problem in the search process. We attempt to use Nash strategy equilibrium (NE) concept in an implementation of an algorithmic mechanism design problem, but our proposed framework is not limited to Nash strategy equilibrium. The evaluation results present the efficiency of the framework. Its primary principle can be implemented in any EC algorithm that needs to consider the strategy selection issue in its optimization process. The final objective of our work is to implement EC design as an algorithmic mechanism design problem and establish EC fundamental aspects based on this perspective.