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Genetic operator

About: Genetic operator is a research topic. Over the lifetime, 2604 publications have been published within this topic receiving 95780 citations.


Papers
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01 Jan 1989
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Abstract: From the Publisher: This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields. Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs. No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required.

33,034 citations

Journal ArticleDOI
TL;DR: This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms.
Abstract: This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms. The tutorial also illustrates genetic search by hyperplane sampling. The theoretical foundations of genetic algorithms are reviewed, include the schema theorem as well as recently developed exact models of the canonical genetic algorithm.

3,967 citations

Journal ArticleDOI
TL;DR: The analogy between genetic algorithms and the search processes in nature is drawn and the genetic algorithm that Holland introduced in 1975 and the workings of GAs are described and surveyed.
Abstract: Genetic algorithms provide an alternative to traditional optimization techniques by using directed random searches to locate optimal solutions in complex landscapes. We introduce the art and science of genetic algorithms and survey current issues in GA theory and practice. We do not present a detailed study, instead, we offer a quick guide into the labyrinth of GA research. First, we draw the analogy between genetic algorithms and the search processes in nature. Then we describe the genetic algorithm that Holland introduced in 1975 and the workings of GAs. After a survey of techniques proposed as improvements to Holland's GA and of some radically different approaches, we survey the advances in GA theory related to modeling, dynamics, and deception. >

2,095 citations

Book
01 Jan 2007
TL;DR: The book contains basic concepts, several applications of Genetic Algorithms and solved Genetic Problems using MATLAB software and C/C++.
Abstract: The book contains basic concepts, several applications of Genetic Algorithms and solved Genetic Problems using MATLAB software and C/C++. The salient features of the book include - detailed explanation of Genetic Algorithm concepts, - numerous Genetic Algorithm Optimization Problems, - study on various types of Genetic Algorithms, - implementation of Optimization problem using C and C++, - simulated solutions for Genetic Algorithm problems using MATLAB 7.0, - brief description on the basics of Genetic Programming, - application case studies on Genetic Algorithm on emerging fields.

1,787 citations

Journal ArticleDOI
TL;DR: Different models of genetic operators and some mechanisms available for studying the behaviour of this type of genetic algorithms are revised and compared.
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 mechanisms available for studying the behaviour of this type of genetic algorithms are revised and compared.

1,190 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20233
20229
202115
202030
201928
201833