Algorithmic Mechanism Design of Evolutionary Computation.

Yan Pei

doi:10.1155/2015/591954

Abstract

We consider algorithmic design, enhancement, and improvement of evolutionary computation as a mechanism design problem. All individuals or several groups of individuals can be considered as self-interested agents. The individuals in evolutionary computation can manipulate parameter settings and operations by satisfying their own preferences, which are defined by an evolutionary computation algorithm designer, rather than by following a fixed algorithm rule. Evolutionary computation algorithm designers or self-adaptive methods should construct proper rules and mechanisms for all agents (individuals) to conduct their evolution behaviour correctly in order to definitely achieve the desired and preset objective(s). As a case study, we propose a formal framework on parameter setting, strategy selection, and algorithmic design of evolutionary computation by considering the Nash strategy equilibrium of a mechanism design in the search process. The evaluation results present the efficiency of the framework. This primary principle can be implemented in any evolutionary computation algorithm that needs to consider strategy selection issues in its optimization process. The final objective of our work is to solve evolutionary computation design as an algorithmic mechanism design problem and establish its fundamental aspect by taking this perspective. This paper is the first step towards achieving this objective by implementing a strategy equilibrium solution (such as Nash equilibrium) in evolutionary computation algorithm.

Highlights

Game theory is the methodology used to research strategic interaction among several self-interested agents [1]
In the fundamental aspects of evolutionary computation (EC) algorithm, approaches and theories of EC will be explained by the corresponding context of game theory and mechanism design
Because the production of differential vectors in Differential evolution (DE) is a critical factor that influences optimization performance and one of our evaluation objectives is to investigate the strategy selection influence of the DE algorithm, we need to reduce the diversity of differential vectors in the evolution so as to reduce population diversity influence and to enhance

Summary

Introduction

Game theory is the methodology used to research strategic interaction among several self-interested agents [1]. Some important concepts, such as type, strategy, and utility, are useful to an understanding of the theoretical framework of game theory. Agent type indicates the preferences of the agent over different outcomes in a game. The utility of an agent determines different allocations and payments under its and other agents’ types and strategy profiles; for example, an agent rationality in game theory is to implement the expected utility to be maximum. An agent will select a strategy that maximizes its expected utility, given its preferences with regard to outcomes, beliefs about the strategies of other agents, and structure of the game

Objectives

Results

Conclusion