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Evolutionary algorithms in theory and practice

Thomas Bäck
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.

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Citations
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Journal ArticleDOI

Sensitivity analysis for indirect measurement in scatterometry and the reconstruction of periodic grating structures

TL;DR: In this paper, numerical algorithms for the determination of periodic surface structures from light diffraction patterns are discussed, and the inverse problem is reformulated as a non-linear operator equation in Euclidean spaces.
Book

Analysis and Applications of Evolutionary Multiobjective Optimization Algorithms

TL;DR: This thesis deals with the analysis and application of evolutionary algorithms for optimization problems with multiple objectives and proposes new selection operators that guarantee the convergence of random¬ ized search strategies to a well-defined discrete solution set with simultaneous consideration of diversity.
Journal ArticleDOI

Cell formation using evolutionary algorithms with certain constraints

TL;DR: In this article, the authors proposed a method using evolutionary algorithms to design cells that can take into account specific constraints (for example, certain machines may have to stay together in the same cell because they will share a common resource or certain machines might have to be separated due to interferences).
Proceedings ArticleDOI

An experimental study of benchmarking functions for genetic algorithms

TL;DR: This paper presents a review and experimental results of major benchmarking functions used for the performance control of genetic algorithms (GAs).