Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
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An introduction to genetic algorithms
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Stabilization of tag-mediated interaction by sexual reproduction in an evolutionary agent system
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An analysis of the behavior of a class of genetic adaptive systems.
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Stabilization of tag-mediated interaction by sexual reproduction in an evolutionary agent system
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MS '08 Proceedings of the 19th IASTED International Conference on Modelling and Simulation
Implicit fitness and heterogeneous preferences in the genetic algorithm
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
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Computational Economics
Paradox Lost: The Evolution of Strategies in Selten's Chain Store Game
Computational Economics
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Agent-based computational economics (ACE) combines elements from economics and computer science. In this paper, we focus on the relation between the evolutionary technique that is used and the economic problem that is modeled. In the field of ACE, economic simulations often derive parameter settings for the evolutionary algorithm directly from the values of the economic model parameters. In this paper, we compare two important approaches that are dominating ACE research and show that the above practice may hinder the performance of the evolutionary algorithm and thereby hinder agent learning. More specifically, we show that economic model parameters and evolutionary algorithm parameters should be treated separately by comparing the two widely used approaches to social learning with respect to their convergence properties and robustness. This leads to new considerations for the methodological aspects of evolutionary algorithm design within the field of ACE.