Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis

Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis (1998)

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by F. Herrera , M. Lozano , J. L. Verdegay

Venue:	Artificial Intelligence Review
Citations:	84 - 17 self

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Datum	Value	Source
TITLE	Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis	user correction - Legacy Corrections
AUTHOR NAME	F. Herrera	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer Science and Artificial Intelligence; University of Granada	SVM HeaderParse 0.2
AUTHOR ADDR	Spain	SVM HeaderParse 0.2
AUTHOR NAME	M. Lozano	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer Science and Artificial Intelligence; University of Granada	SVM HeaderParse 0.2
AUTHOR ADDR	Spain	SVM HeaderParse 0.2
AUTHOR NAME	J. L. Verdegay	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer Science and Artificial Intelligence; University of Granada	SVM HeaderParse 0.2
AUTHOR ADDR	Spain	SVM HeaderParse 0.2
ABSTRACT	. Genetic algorithms play a significant role, as search techniques for handling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the natural evolution principles of populations. These algorithms process a population of chromosomes, which represent search space solutions, with three operations: selection, crossover and mutation. Under its initial formulation, the search space solutions are coded using the binary alphabet. However, the good properties related with these algorithms do not stem from the use of this alphabet; other coding types have been considered for the representation issue, such as real coding, which would seem particularly natural when tackling optimization problems of parameters with variables in continuous domains. In this paper we review the features of real-coded genetic algorithms. Different models of genetic operators and some me...	user correction - Legacy Corrections
YEAR	1998	INFERENCE
VENUE	Artificial Intelligence Review	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	265--319	INFERENCE
VOLUME	12	INFERENCE
CITATIONS	71 found	ParsCit 1.0