• Publications
  • Influence
An Analysis of Cooperative Coevolutionary Algorithms A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at George Mason University
AN ANALYSIS OF COOPERATIVE COEVOLUTIONARY ALGORITHMS R. Paul Wiegand George Mason University, 2003 Thesis Director: Dr. Kenneth A. De Jong Coevolutionary algorithms behave in very complicated, oftenExpand
An empirical analysis of collaboration methods in cooperative coevolutionary algorithms
This paper offers an empirical analysis of various types of collaboration mechanisms and presents some basic advice about how to choose a mechanism which is appropriate for a particular problem. Expand
An analysis of cooperative coevolutionary algorithms
A new view of the CCEAs is offered that includes analysis-guided suggestions for how a traditional CCEA might be modified to be better suited for optimization tasks, or might be applied to more appropriate tasks, given the nature of its dynamics. Expand
Biasing Coevolutionary Search for Optimal Multiagent Behaviors
This work biasing a cooperative CEA in such a way that the fitness of an individual is based partly on the result of interactions with other individuals, and partly on an estimate of the best possible reward for that individual if partnered with its optimal collaborator. Expand
Exploring the Explorative Advantage of the Cooperative Coevolutionary (1+1) EA
A systematic comparison between the expected optimization times of this coevolutionary algorithm and the ordinary (1+1) EA is presented, and it is shown that the cooperative coev evolutionary approach comes with new explorative possibilities that can lead to an immense speed-up of the optimization. Expand
Coevolutionary Principles
This chapter outlines the ends and means of coevolutionary algorithms: what they are meant to find, and how they should find it. Expand
Black-box search by elimination of fitness functions
Though in its early stages, it is believed that there is utility in search methods based on ideas from the elimination of functions method, and that the viewpoint provides promise and new insight about black-box optimization. Expand
Analyzing cooperative coevolution with evolutionary game theory
This paper introduces their analysis framework, explaining how and why EGT models may be generated, and demonstrates that using the framework, a better understanding for the degree to which coevolutionary algorithms can be used for optimization can be achieved. Expand
Improving Coevolutionary Search for Optimal Multiagent Behaviors
This paper examines the idea of modifying traditional coevolution, biasing it to search for maximal rewards, and concludes that biasing can help coev evolution find better results in some multiagent problem domains. Expand
A Sensitivity Analysis of a Cooperative Coevolutionary Algorithm Biased for Optimization
This paper investigates how sensitivity to the degree of bias (set in advance) is affected by certain algorithmic and problem properties, and proposes a stochastic alternative which alleviates this problem. Expand