Theoretical Computer Science - Natural computing
A Cooperative Coevolutionary Approach to Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Upper and Lower Bounds for Randomized Search Heuristics in Black-Box Optimization
Theory of Computing Systems
Black-box search by elimination of fitness functions
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Analysis of an asymmetric mutation operator
Evolutionary Computation
Black-box search by unbiased variation
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Faster black-box algorithms through higher arity operators
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
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This paper extends previous work that presented an algorithm called Optimal Elimination of Fitness Functions (OEFF). OEFF is by itself conditionally optimal over all problem classes, albeit impractical. Here, we complement this algorithm with an optimal sample selection strategy that removes the condition. Consequently, the performance of this combined algorithm over a domain is the black-box complexity of that domain, providing a new technique for deriving black-box complexity. Additionally, we suggest techniques to perform runtime analysis of our extended OEFF algorithm. We discuss how those techniques can be used to build an algorithm that is targeted, practical, yet equivalent to OEFF with optimal sampling over the target domain. This is demonstrated on the Generalized Leading Ones problem domain, where we derive black-box complexity and develop an optimal algorithm.