Black box search: framework and methods
Black box search: framework and methods
Focused no free lunch theorems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Evolutionary Computation
Black-box search by unbiased variation
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Representation invariant genetic operators
Evolutionary Computation
Faster black-box algorithms through higher arity operators
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Reducing the arity in unbiased black-box complexity
Proceedings of the 14th annual conference on Genetic and evolutionary computation
The automatic generation of mutation operators for genetic algorithms
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Black-box complexities of combinatorial problems
Theoretical Computer Science
Black-box complexity: from complexity theory to playing mastermind
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Evolutionary Computation
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We formalize the concept of an unbiased black box algorithm, which generalises the idea previously introduced by Lehre and Witt. Our formalization of bias relates to the symmetry group of the problem class under consideration, establishing a connection with previous work on No Free Lunch. Our definition is motivated and justified by a series of results, including the outcome that given a biased algorithm, there exists a corresponding unbiased algorithm with the same expected behaviour (over the problem class) and equal or better worst-case performance. For the case of evolutionary algorithms, it is already known how to construct unbiased mutation and crossover operators, and we summarise those results.