Coevolutionary search among adversaries
Coevolutionary search among adversaries
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
An analysis of two-population coevolutionary computation
An analysis of two-population coevolutionary computation
A no-free-lunch framework for coevolution
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
New Approaches to Coevolutionary Worst-Case Optimization
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Unbiased coevolutionary solution concepts
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Monotonicity versus performance in co-optimization
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Analysis of coevolution for worst-case optimization
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Free lunches in pareto coevolution
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
| Hi-index | 0.00 |
Co-optimization problems involve one or more search spaces and a means of assessing interactions between entities in these spaces. Assessing a potential solution requires aggregating in some way the outcomes of a very large or infinite number of such interactions. This layer of complexity presents difficulties for algorithm design that are not encountered in ordinary optimization. For example, what a co-optimization algorithm should output is not at all obvious. Theoretical research has shown that some output selection mechanisms yield better overall performance than others and described an optimal mechanism. This mechanism was shown to be strictly better than a greedy method in common use, but appeared prohibitive from a practical standpoint. In this paper we exhibit the optimal output mechanism for a particular class of co-optimization problems and a certain definition of better overall performance, and provide quantitative characterizations of domains for which this optimal mechanism becomes straightforward to implement. We conclude with a discussion of potential extensions of this work to other problem classes and other views on performance.