Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
The theory of evolution strategies
The theory of evolution strategies
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Accelerating the Evolutionary-Gradient-Search Procedure: Individual Step Sizes
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
On the "Explorative Power" of ES/EP-like Algorithms
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Evolutionary computation: comments on the history and current state
IEEE Transactions on Evolutionary Computation
Evolutionary algorithms and gradient search: similarities anddifferences
IEEE Transactions on Evolutionary Computation
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Recent advances in the theory of evolutionary algorithms have indicated that a hybrid method known as the evolutionary-gradient-search procedure yields superior performance in comparison to contemporary evolution strategies. But the theoretical analysis also indicates a noticeable performance loss in the presence of noise (i.e., noisy fitness evaluations). This paper aims at understanding the reasons for this observable performance loss. It also proposes some modifications, called inverse mutations, to make the process of estimating the gradient direction more noise robust.