Games, Supply Chains, and Automatic Strategy Discovery Using Evolutionary Computation

Timothy Gosling, Nanlin Jin, Edward Tsang

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

The use of evolutionary computation is significant for the development and optimisation of strategies for dynamic and uncertain situations. This chapter introduces three cases in which evolutionary computation has already been used successfully for strategy generation in the form of work on the Iterated Prisoner’s Dilemma, Rubinstein’s alternating offers bargaining model, and the simple supply chain model. The first two of these show how evolutionary computation has been applied to extensively studied, well-known problems. The last of these demonstrates how recent statistical approaches to evolutionary computation have been applied to more complex supply chain situations that traditional game-theoretical analysis has been unable to tackle. The authors hope that the chapter will promote this approach, motivate further work in this area, and provide a guide to some of the subtleties involved in applying evolutionary computation to different problems.
Original languageEnglish
Title of host publicationHandbook of Research on Nature-Inspired Computing for Economics and Management
Place of PublicationHershey, PA
PublisherIGI Global
Pages572-588
ISBN (Print)978-1-59140-9847
DOIs
Publication statusPublished - 2006

Fingerprint Dive into the research topics of 'Games, Supply Chains, and Automatic Strategy Discovery Using Evolutionary Computation'. Together they form a unique fingerprint.

Cite this