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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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A two-stage evolutionary process for designing TSK fuzzy rule-based systems
Oscar Cordón,Francisco Herrera +1 more
TL;DR: A two-stage evolutionary process for designing TSK fuzzy rule-based systems from examples combining a generation stage based on a (mu, lambda)-evolution strategy and a refinement stage in which both the antecedent and consequent parts of the fuzzy rules in this previous knowledge base are adapted by a hybrid evolutionary process.
Journal ArticleDOI
A simple self-adaptive Differential Evolution algorithm with application on the ALSTOM gasifier
Amin Nobakhti,Hong Wang +1 more
TL;DR: This sensitivity to its control parameters is studied here and a simple randomised self-adaptive scheme is proposed for the DE mutation weighting factor F.
A modified firefly algorithm for UCAV path planning
TL;DR: A new modified firefly algorithm (MFA) is proposed to solve the UCAV path planning problem, and a modification is applied to exchange information between top fireflies during the process of the light intensity updating to accelerate the global convergence speed while preserving the strong robustness of the basic FA.
Proceedings ArticleDOI
Evolutionary algorithms for multi-objective optimization: performance assessments and comparisons
TL;DR: This paper aims to analyze the strength and weakness of different evolutionary methods proposed in the literature for multi-objective MO optimization, and proposes a few useful performance measures for better and comprehensive examination of each approach both quantitatively and qualitatively.
Journal ArticleDOI
Multiple trial vectors in differential evolution for engineering design
TL;DR: A modified version of the differential evolution algorithm is presented to allow each parent vector in the population to generate more than one trial (child) vector at each generation and therefore to increase its probability of generating a better one.