Noisy Optimization with Evolution Strategies (Genetic Algorithms and Evolutionary Computation Series)
Dirk V. Arnold, Hans-Georg Beyer, David E. Goldberg (Editor)Books.org participates in affiliate programs including Bookshop.org and the Amazon Services LLC Associates Program. We may earn a commission from qualifying purchases made through links on this page, at no additional cost to you.
Overview
Noise is a common factor in most real-world optimization problems. Sources of noise can include physical measurement limitations, shastic simulation models, incomplete sampling of large spaces, and human-computer interaction. Evolutionary algorithms are general, nature-inspired heuristics for numerical search and optimization that are frequently observed to be particularly robust with regard to the effects of noise.Noisy Optimization with Evolution Strategies contributes to the understanding of evolutionary optimization in the presence of noise by investigating the performance of evolution strategies, a type of evolutionary algorithm frequently employed for solving real-valued optimization problems. By considering simple noisy environments, results are obtained that describe how the performance of the strategies scales with both parameters of the problem and of the strategies considered. Such scaling laws allow for comparisons of different strategy variants, for tuning evolution strategies for maximum performance, and they offer insights and an understanding of the behavior of the strategies that go beyond what can be learned from mere experimentation.
This first comprehensive work on noisy optimization with evolution strategies investigates the effects of systematic fitness overvaluation, the benefits of distributed populations, and the potential of genetic repair for optimization in the presence of noise. The relative robustness of evolution strategies is confirmed in a comparison with other direct search algorithms.
Noisy Optimization with Evolution Strategies is an invaluable resource for researchers and practitioners of evolutionary algorithms.
Synopsis
Noise is a common factor in most real-world optimization problems. Sources of noise can include physical measurement limitations, stochastic simulation models, incomplete sampling of large spaces, and human-computer interaction. Evolutionary algorithms are general, nature-inspired heuristics for numerical search and optimization that are frequently observed to be particularly robust with regard to the effects of noise.
Noisy Optimization with Evolution Strategies contributes to the understanding of evolutionary optimization in the presence of noise by investigating the performance of evolution strategies, a type of evolutionary algorithm frequently employed for solving real-valued optimization problems. By considering simple noisy environments, results are obtained that describe how the performance of the strategies scales with both parameters of the problem and of the strategies considered. Such scaling laws allow for comparisons of different strategy variants, for tuning evolution strategies for maximum performance, and they offer insights and an understanding of the behavior of the strategies that go beyond what can be learned from mere experimentation.
This first comprehensive work on noisy optimization with evolution strategies investigates the effects of systematic fitness overvaluation, the benefits of distributed populations, and the potential of genetic repair for optimization in the presence of noise. The relative robustness of evolution strategies is confirmed in a comparison with other direct search algorithms.
Noisy Optimization with Evolution Strategies is an invaluable resource for researchers and practitioners of evolutionary algorithms.
Booknews
Summarizes many of the results on evolution strategies in continuous, noisy search spaces obtained previously, and extends them in a number of ways. Arnold (University of Dortmund) studies the effects that noise has on the local performance of several variants, and employs both a linear function with constant noise strength and a spherically symmetric objective function with fitness proportionate noise strength. Specifically, he investigates the influence of distributed populations on the performance of evolution strategies, and the effects of global intermediate recombination in the presence of noise. Annotation c. Book News, Inc., Portland, OR
Editorials
Summarizes many of the results on evolution strategies in continuous, noisy search spaces obtained previously, and extends them in a number of ways. Arnold (University of Dortmund) studies the effects that noise has on the local performance of several variants, and employs both a linear function with constant noise strength and a spherically symmetric objective function with fitness proportionate noise strength. Specifically, he investigates the influence of distributed populations on the performance of evolution strategies, and the effects of global intermediate recombination in the presence of noise. Annotation c. Book News, Inc., Portland, OR