@article{2006002,
abstract = {In this paper, we present a new paradigm of searching optimal strategies in the game of iterated prisoner's dilemma (IPD) using multiple-objective evolutionary algorithms. This method is more useful than the existing approaches, because it not only produces strategies that perform better in the iterated game but also finds a family of nondominated strategies, which can be analyzed to decipher properties a strategy should have to win the game in a more satisfactory manner. We present the results obtained by this new method and discuss sub-strategies found to be common among nondominated strategies. The multiobjective treatment of the IPD problem demonstrated here can be applied to other similar game-playing tasks. {\textcopyright} 2009 IEEE.},
author = {Mittal, Shashi and Deb, Kalyanmoy},
doi = {10.1109/TEVC.2008.2009459},
issn = {1089778X},
journal = {IEEE Transactions on Evolutionary Computation},
keywords = {Evolutionary algorithms,Games,Multiobjective optimization,Prisoner's dilemma},
number = {3},
pages = {554--565},
title = {{Optimal strategies of the iterated prisoner's dilemma problem for multiple conflicting objectives}},
volume = {13},
year = {2009}
}