scispace - formally typeset
Search or ask a question
Book

Genetic Algorithms

About: The article was published on 2002-01-01 and is currently open access. It has received 17039 citations till now.
Citations
More filters
Journal ArticleDOI
TL;DR: This historical survey compactly summarizes relevant work, much of it from the previous millennium, review deep supervised learning, unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.

14,635 citations


Cites background from "Genetic Algorithms"

  • ... estimated (and then exploited) through repeated NN evaluations. RL NNs can also be evolved through Evolutionary Algorithms (EAs) (Rechenberg, 1971; Schwefel, 1974; Holland, 1975; Fogel et al., 1966; Goldberg, 1989) in a series of trials. Here several policies are represented by a population of NNs improved through mutations and/or repeated recombinations of the population’s fittest individuals (e.g., Montana and...

    [...]

Journal ArticleDOI
TL;DR: The results of the classical engineering design problems and real application prove that the proposed GWO algorithm is applicable to challenging problems with unknown search spaces.

10,082 citations

Journal ArticleDOI
TL;DR: Optimization results prove that the WOA algorithm is very competitive compared to the state-of-art meta-heuristic algorithms as well as conventional methods.

7,090 citations

Proceedings ArticleDOI
01 Dec 2009
TL;DR: A new meta-heuristic algorithm, called Cuckoo Search (CS), is formulated, based on the obligate brood parasitic behaviour of some cuckoo species in combination with the Lévy flight behaviour ofSome birds and fruit flies, for solving optimization problems.
Abstract: In this paper, we intend to formulate a new meta-heuristic algorithm, called Cuckoo Search (CS), for solving optimization problems. This algorithm is based on the obligate brood parasitic behaviour of some cuckoo species in combination with the Levy flight behaviour of some birds and fruit flies. We validate the proposed algorithm against test functions and then compare its performance with those of genetic algorithms and particle swarm optimization. Finally, we discuss the implication of the results and suggestion for further research.

5,521 citations


Cites background or methods from "Genetic Algorithms"

  • ....,d. The global mimimum is f∗ ≈ −1.801 for d = 2, while f∗ ≈ −4.6877 for d = 5. 4.3 Comparison of CS with PSO and GA Recent studies indicate that PSO algorithms can outperform genetic algorithms (GA) [8] and other conventional algorithms for many optimization problems. This can partly be attributed to the broadcasting ability of the current best estimates which potentially gives better and quicker co...

    [...]

  • ...l fireflies [2, 3, 6, 21, 22]. These nature-inspired metaheuristic algorithms have been used in a wide range of optimization problems, including NP-hard problems such as the travelling salesman problem [2, 3, 6, 8, 10, 21]. The power of almost all modern metaheuristics comes from the fact that they imitate the best feature in nature, especially biological systems evolved from natural selection over millions of years. T...

    [...]

Book
01 Feb 2008
TL;DR: This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search, and firefly algorithms.
Abstract: Modern metaheuristic algorithms such as bee algorithms and harmony search start to demonstrate their power in dealing with tough optimization problems and even NP-hard problems. This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search, and firefly algorithms. We also briefly introduce the photosynthetic algorithm, the enzyme algorithm, and Tabu search. Worked examples with implementation have been used to show how each algorithm works. This book is thus an ideal textbook for an undergraduate and/or graduate course. As some of the algorithms such as the harmony search and firefly algorithms are at the forefront of current research, this book can also serve as a reference book for researchers.

3,626 citations

References
More filters
Book
01 Sep 1988
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
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

52,797 citations

Book
01 Jan 1992
TL;DR: GAs and Evolution Programs for Various Discrete Problems, a Hierarchy of Evolution Programs and Heuristics, and Conclusions.
Abstract: 1 GAs: What Are They?.- 2 GAs: How Do They Work?.- 3 GAs: Why Do They Work?.- 4 GAs: Selected Topics.- 5 Binary or Float?.- 6 Fine Local Tuning.- 7 Handling Constraints.- 8 Evolution Strategies and Other Methods.- 9 The Transportation Problem.- 10 The Traveling Salesman Problem.- 11 Evolution Programs for Various Discrete Problems.- 12 Machine Learning.- 13 Evolutionary Programming and Genetic Programming.- 14 A Hierarchy of Evolution Programs.- 15 Evolution Programs and Heuristics.- 16 Conclusions.- Appendix A.- Appendix B.- Appendix C.- Appendix D.- References.

12,212 citations


"Genetic Algorithms" refers background in this paper

  • ...There are many publications which give excellent introductions to genetic algorithms: see Holland (1975), Davis (1987), Goldberg (1989b), Davis (1991), Beasley et al. (1993), Forrest (1993), Reeves (1995), Michalewicz (1996), Mitchell (1996), Falkenauer (1998), Coley (1999), and Man et al. (1999)....

    [...]

Journal ArticleDOI
TL;DR: An Introduction to Genetic Algorithms as discussed by the authors is one of the rare examples of a book in which every single page is worth reading, and the author, Melanie Mitchell, manages to describe in depth many fascinating examples as well as important theoretical issues.
Abstract: An Introduction to Genetic Algorithms is one of the rare examples of a book in which every single page is worth reading. The author, Melanie Mitchell, manages to describe in depth many fascinating examples as well as important theoretical issues, yet the book is concise (200 pages) and readable. Although Mitchell explicitly states that her aim is not a complete survey, the essentials of genetic algorithms (GAs) are contained: theory and practice, problem solving and scientific models, a \"Brief History\" and \"Future Directions.\" Her book is both an introduction for novices interested in GAs and a collection of recent research, including hot topics such as coevolution (interspecies and intraspecies), diploidy and dominance, encapsulation, hierarchical regulation, adaptive encoding, interactions of learning and evolution, self-adapting GAs, and more. Nevertheless, the book focused more on machine learning, artificial life, and modeling evolution than on optimization and engineering.

7,098 citations


"Genetic Algorithms" refers background in this paper

  • ...There are many publications which give excellent introductions to genetic algorithms: see Holland (1975), Davis (1987), Goldberg (1989b), Davis (1991), Beasley et al. (1993), Forrest (1993), Reeves (1995), Michalewicz (1996), Mitchell (1996), Falkenauer (1998), Coley (1999), and Man et al. (1999)....

    [...]

Book
01 Jan 1991
TL;DR: This book sets out to explain what genetic algorithms are and how they can be used to solve real-world problems, and introduces the fundamental genetic algorithm (GA), and shows how the basic technique may be applied to a very simple numerical optimisation problem.
Abstract: This book sets out to explain what genetic algorithms are and how they can be used to solve real-world problems. The first objective is tackled by the editor, Lawrence Davis. The remainder of the book is turned over to a series of short review articles by a collection of authors, each explaining how genetic algorithms have been applied to problems in their own specific area of interest. The first part of the book introduces the fundamental genetic algorithm (GA), explains how it has traditionally been designed and implemented and shows how the basic technique may be applied to a very simple numerical optimisation problem. The basic technique is then altered and refined in a number of ways, with the effects of each change being measured by comparison against the performance of the original. In this way, the reader is provided with an uncluttered introduction to the technique and learns to appreciate why certain variants of GA have become more popular than others in the scientific community. Davis stresses that the choice of a suitable representation for the problem in hand is a key step in applying the GA, as is the selection of suitable techniques for generating new solutions from old. He is refreshingly open in admitting that much of the business of adapting the GA to specific problems owes more to art than to science. It is nice to see the terminology associated with this subject explained, with the author stressing that much of the field is still an active area of research. Few assumptions are made about the reader's mathematical background. The second part of the book contains thirteen cameo descriptions of how genetic algorithmic techniques have been, or are being, applied to a diverse range of problems. Thus, one group of authors explains how the technique has been used for modelling arms races between neighbouring countries (a non- linear, dynamical system), while another group describes its use in deciding design trade-offs for military aircraft. My own favourite is a rather charming account of how the GA was applied to a series of scheduling problems. Having attempted something of this sort with Simulated Annealing, I found it refreshing to see the authors highlighting some of the problems that they had encountered, rather than sweeping them under the carpet as is so often done in the scientific literature. The editor points out that there are standard GA tools available for either play or serious development work. Two of these (GENESIS and OOGA) are described in a short, third part of the book. As is so often the case nowadays, it is possible to obtain a diskette containing both systems by sending your Visa card details (or $60) to an address in the USA.

6,758 citations


"Genetic Algorithms" refers background in this paper

  • ...There are many publications which give excellent introductions to genetic algorithms: see Holland (1975), Davis (1987), Goldberg (1989b), Davis (1991), Beasley et al. (1993), Forrest (1993), Reeves (1995), Michalewicz (1996), Mitchell (1996), Falkenauer (1998), Coley (1999), and Man et al. (1999)....

    [...]

Journal ArticleDOI
TL;DR: An introduction to population genetics theory, An introduction to Population Genetics Theory, Population Genetics theory, Population genetics theory as discussed by the authors, Population genetics, population genetics, and population genetics theories, Population Genetic Theory
Abstract: An introduction to population genetics theory , An introduction to population genetics theory , مرکز فناوری اطلاعات و اطلاع رسانی کشاورزی

4,817 citations