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Evolutionary algorithms in theory and practice
TLDR
In this work, the author compares the three most prominent representatives of evolutionary algorithms: genetic algorithms, evolution strategies, and evolutionary programming within a unified framework, thereby clarifying the similarities and differences of these methods.About:
The article was published on 1996-01-01 and is currently open access. It has received 2679 citations till now. The article focuses on the topics: Evolutionary music & Evolutionary programming.read more
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Journal ArticleDOI
Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels
TL;DR: The extensive use of the uncertainty information of predictions for screening the candidate solutions makes it possible to significantly reduce the computational cost of singleand multiobjective EA.
Journal ArticleDOI
A framework for evolutionary optimization with approximate fitness functions
TL;DR: A framework for managing approximate models in generation-based evolution control is proposed, well suited for parallel evolutionary optimization, which is able to guarantee the correct convergence of the evolutionary algorithm, as well as to reduce the computation cost as much as possible.
Book ChapterDOI
Iterated Local Search: Framework and Applications
TL;DR: The purpose here is to give an accessible description of the underlying principles of iterated local search and a discussion of the main aspects that need to be taken into account for a successful application of it.
Journal ArticleDOI
A simple multimembered evolution strategy to solve constrained optimization problems
TL;DR: The proposed approach to solve global nonlinear optimization problems uses a simple diversity mechanism based on allowing infeasible solutions to remain in the population to find the global optimum despite reaching reasonably fast the feasible region of the search space.
Book ChapterDOI
Fast Evolution Strategies
TL;DR: It is shown empirically that the new evolution strategy based on Cauchy mutation outperforms the classical evolution strategy on most of the 23 benchmark problems tested in this paper.