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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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Book ChapterDOI
A Superior Evolutionary Algorithm for 3-SAT
TL;DR: The results show that the adaptive EA outperforms the other two approaches to Boolean satisfiability problems, and suggests that this EA is not only a good solver for satisfiable problems, but for constraint satisfaction problems in general.
Journal ArticleDOI
Hierarchical distributed genetic algorithms
TL;DR: A hierarchical model of distributed genetic algorithms in which a higher level distributed genetic algorithm joins different simple distributed Genetic algorithms is presented, which consistently outperform equivalent sequential genetic algorithms and simple genetic algorithms.
Journal ArticleDOI
Design of Graph-Based Evolutionary Algorithms: A Case Study for Chemical Process Networks
TL;DR: The adaptation of evolutionary algorithms (EAs) to the structural optimization of chemical engineering plants is described, using rigorous process simulation combined with realistic costing procedures to calculate target function values.
Journal ArticleDOI
Genetic Mining of HTML Structures for Effective Web-Document Retrieval
Sun Kim,Byoung-Tak Zhang +1 more
TL;DR: A genetic algorithm is described that learns the importance factors of HTML tags which are used to re-rank the documents retrieved by standard weighting schemes, which tends to move relevant documents to upper ranks, which is especially important in interactive Web-information retrieval environments.