Co-evolving parasites improve simulated evolution as an optimization procedure
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
An analysis of cooperative coevolutionary algorithms
An analysis of cooperative coevolutionary algorithms
Solution concepts in coevolutionary algorithms
Solution concepts in coevolutionary algorithms
The MaxSolve algorithm for coevolution
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Monotonic solution concepts in coevolution
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
The parallel Nash Memory for asymmetric games
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A no-free-lunch framework for coevolution
Proceedings of the 10th annual conference on Genetic and evolutionary computation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Analysis of coevolution for worst-case optimization
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
On the practicality of optimal output mechanisms for co-optimization algorithms
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
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The primary concern of this paper is the question: Is the goal of an optimal well behaved coevolutionary algorithm attainable? We approach this question from the point of view of the No Free Lunch (NFL) theorem. The NFL theorem has been shown to, in general, not hold in coevolution and as such we can hope for optimal (in the NFL sense) coevolutionary algorithms. We attempt to shed light on this question by investigating the relationship between Ficici's notion of monotonicity and algorithm performance by introducing the notion of solution concept bias. Informally, the unbiased solution concepts are those for which the NFL theorem holds. We show that the notion of solution concept bias and Ficici's notion of monotonicity are orthogonal in the sense that all possible combinations of bias and monotonicity are possible. We also explore some possible consequences and trade-offs which might arise in coevolutionary algorithm design due to different combinations of bias and monotonicity. For example, in biased monotonic solution concepts there may be a trade-off between the guarantee of good algorithmic behavior and optimality. An algorithm which improves monotonically may be suboptimal. The results presented in this paper raise the possibility that the goals of monotonicity and an optimality may be in conflict and bring to light the question: Which is more important, the quality of the solution produced by a coevolutionary algorithm, or the dynamics by which that solution was obtained?