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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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Genetic Algorithms for Multiple-Choice Optimisation Problems
TL;DR: It is shown that problem-specific knowledge can significantly enhance performance, but that the choice of information and the way it is included are important factors for success, and that the main theme of this work is to balance feasibility and cost of solutions.
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Solving the tool switching problem with memetic algorithms
TL;DR: The memetic algorithm endowed with steepest ascent hill climbing search yields the best results, performing synergistically better than its stand-alone constituents, and providing better results than the rest of the algorithms (including those returned by an effective ad hoc beam search heuristic defined in the literature for this problem).
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Obtaining the phase of an interferogram by use of an evolution strategy: Part I.
TL;DR: This work finds the wave-front aberrations by transforming the problem of fitting a polynomial into an optimization problem, which is then solved using an evolutionary algorithm.
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Recurrent fuzzy network design using hybrid evolutionary learning algorithms
Chia-Feng Juang,I-Fang Chung +1 more
TL;DR: This paper proposes a recurrent fuzzy network design using the hybridization of a multigroup genetic algorithm and particle swarm optimization (R-MGAPSO), which is the Takagi-Sugeno-Kang (TSK)-type recurrent fuzzynetwork (TRFN), in which each fuzzy rule comprises spatial and temporal sub-rules.
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A comparative study of genetic operators for controller parameter optimisation
TL;DR: In this paper, a set of 18 GAs, which combine three different selection methods, three probabilities of crossover and two probabilities of mutation, are used to solve four controller parameter optimisation problems and the results obtained are compared.