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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

A novel hybrid bat algorithm for solving continuous optimization problems

TL;DR: A novel Hybrid Bat Algorithm (HBA) is proposed to improve the performance of BA and three modification methods are incorporated into the standard BA to enhance the local search capability and the ability to escape from local optimum traps.
Journal ArticleDOI

Feature selection techniques for intrusion detection using non-bio-inspired and bio-inspired optimization algorithms

TL;DR: This work provides a survey of feature selection techniques for IDS, including bio-inspired algorithms, includingBio-inspired optimization algorithms have been used for feature selection.
Book ChapterDOI

An Introduction to Evolutionary Programming

TL;DR: This paper offers an introduction to evolutionary programming, and indicates its relationship to other methods of evolutionary computation, specifically genetic algorithms and evolution strategies.
Journal ArticleDOI

Determining relative importance and effective settings for genetic algorithm control parameters

TL;DR: It was found that crossover most significantly influenced GA success, followed by mutation rate and population size and then by rerandomization point and elite selection, which are robust over 60 numerical optimization problems.