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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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Proceedings ArticleDOI

Collaborative evolutionary multi-project resource scheduling

TL;DR: This work justifies and describes a collaborative evolutionary scheduling system, which enables the search to be guided by both the standard schedule quality criteria and also the master scheduler's non-formalised knowledge and experience.
Book ChapterDOI

Bayesian Evolutionary Optimization Using Helmholtz Machines

TL;DR: A Bayesian evolutionary algorithm (BEA) that learns the sample distribution by a probabilistic graphical model known as Helmholtz machines is presented, which outperforms the simple genetic algorithm.
Journal ArticleDOI

GA Guided Cluster Based Fuzzy Decision Tree for Reactive Ion Etching Modeling: A Data Mining Approach

TL;DR: A novel data mining technique, genetically guided cluster based fuzzy decision tree (GCFDT), is introduced for the mining task and it is employed for building the predictive process models of reactive ion etching (RIE) with the aid of optical emission spectroscopy (OES) signals.
Book ChapterDOI

Survivable Network Design with an Evolution Strategy

Volker Nissen, +1 more
TL;DR: A novel evolutionary approach to survivable network design is presented when considering both economics and reliability and clearly outperforms existing benchmark results by genetic algorithms.
Journal ArticleDOI

Automatically composing and parameterizing skills by evolving Finite State Automata

TL;DR: The results show that an FSA generated in simulation can be directly applied to a real robot without modifications and the evolved FSA is robust to the noise and the uncertainty arising from real-world sensors.