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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
Performance assessment of the metaheuristic optimization algorithms: an exhaustive review
TL;DR: It is of utmost importance to use a correct tool for measuring the performance of the diverse set of metaheuristic algorithms to derive an appropriate judgment on the superiority of the algorithms and also to validate the claims raised by researchers for their specific objectives.
Journal ArticleDOI
Computer-aided optimization of nonlinear microwave circuits with the aid of electromagnetic simulation
TL;DR: Some modern trends in nonlinear (NL) microwave circuit optimization based on electromagnetic (EM) simulation are discussed, including space mapping, domain partitioning, and neural-network modeling of the passive subnetwork and/or of its most critical parts.
Journal ArticleDOI
A Comparison Study of Self-Adaptation in Evolution Strategies and Real-Coded Genetic Algorithms
TL;DR: The self-adaptive characteristics such as translation, enlargement, focusing, and directing of the distribution of children generated by the ES and the RCGA are examined through experiments.
Journal ArticleDOI
A constrained, globalized, and bounded Nelder–Mead method for engineering optimization
TL;DR: A fixed cost local search, which sequentially becomes global, is developed in this work, and is particularly adapted to tackling multimodal, discontinuous, constrained optimization problems, for which it is uncertain that a global optimization can be afforded.
Book ChapterDOI
Introduction to creative evolutionary systems
Peter J. Bentley,David Corne +1 more
TL;DR: In computer science and in artificial intelligence, when we use a search algorithm, we define a computational problem in terms of a search space, which can be viewed as a massive collection of potential solutions to the problem as mentioned in this paper.