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

Gosling, Timothy, Jin, Nanlin and Tsang, Edward (2006) Games, Supply Chains, and Automatic Strategy Discovery Using Evolutionary Computation. In: Handbook of Research on Nature-Inspired Computing for Economics and Management. IGI Global, Hershey, PA, pp. 572-588. ISBN 978-1-59140-9847

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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.

Item Type: Book Section
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 11 Aug 2014 11:35
Last Modified: 12 Oct 2019 22:29

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