On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
On classes of functions for which No Free Lunch results hold
Information Processing Letters
Solution concepts in coevolutionary algorithms
Solution concepts in coevolutionary algorithms
A Mathematical Theory of Communication
A Mathematical Theory of Communication
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Faster black-box algorithms through higher arity operators
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Too fast unbiased black-box algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Towards a complexity theory of randomized search heuristics: ranking-based black-box complexity
CSR'11 Proceedings of the 6th international conference on Computer science: theory and applications
Reducing the arity in unbiased black-box complexity
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Lessons from the black-box: fast crossover-based genetic algorithms
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Black-box complexity: from complexity theory to playing mastermind
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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In black-box optimization an algorithm must solve one of many possible functions, though the precise instance is unknown. In practice, it is reasonable to assume that an algorithm designer has some basic knowledge of the problem class in order to choose appropriate methods. In traditional approaches, one focuses on how to select samples and direct search to minimize the number of function evaluations to find an optima. As an alternative view, we consider search processes as determining which function in the problem class is the unknown target function by using samples to eliminate candidate functions from the set. We focus on the efficiency of this elimination process and construct an idealized method for optimal elimination of fitness functions. From this, we place our technique in context by relating performances of our idealized method to common search heuristics (e.g., (1+1) EA), and showing how our ideas relate to No Free Lunch theory. In our discussion, we address some of the practicalities of our method. Though in its early stages, we believe that there is utility in search methods based on ideas from our elimination of functions method, and that our viewpoint provides promise and new insight about black-box optimization.