Randomized algorithms
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Upper and Lower Bounds for Randomized Search Heuristics in Black-Box Optimization
Theory of Computing Systems
Worst-case and average-case approximations by simple randomized search heuristics
STACS'05 Proceedings of the 22nd annual conference on Theoretical Aspects of Computer Science
Evolutionary learning with kernels: a generic solution for large margin problems
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Controlling overfitting with multi-objective support vector machines
Proceedings of the 9th annual conference on Genetic and evolutionary computation
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Different fitness functions describe different problems. Hence, certain fitness transformations can lead to easier problems although they are still a model of the considered problem. In this paper, the class of neutral transformations for a simple rank-based evolutionary algorithm (EA) is described completely, i.e., the class of functions that transfers easy problems for this EA in easy ones and difficult problems in difficult ones. Moreover, the class of neutral transformations for this population-based EA is equal to the black-box neutral transformations. Hence, it is a proper superset of the corresponding class for an EA based on fitness-proportional selection, but it is a proper subset of the class for random search. Furthermore, the minimal and maximal classes of neutral transformations are investigated in detail.