A Genetic Algorithm for Function Optimization: A Matlab Implementation

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A Genetic Algorithm for Function Optimization: A Matlab Implementation

A Genetic Algorithm for Function Optimization: A Matlab Implementation

01 Jan 2001-

TL;DR: The genetic algorithm using a oat representation is found to be superior to both a binary genetic algorithm and simulated annealing in terms of e ciency and quality of solution.

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Abstract: A genetic algorithm implemented in Matlab is presented. Matlab is used for the following reasons: it provides many built in auxiliary functions useful for function optimization; it is completely portable; and it is e cient for numerical computations. The genetic algorithm toolbox developed is tested on a series of non-linear, multi-modal, non-convex test problems and compared with results using simulated annealing. The genetic algorithm using a oat representation is found to be superior to both a binary genetic algorithm and simulated annealing in terms of e ciency and quality of solution. The use of genetic algorithm toolbox as well as the code is introduced in the paper.

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Journal Article•DOI•

Ensembling neural networks: many could be better than all

[...]

Zhi-Hua Zhou¹, Jianxin Wu¹, Wei Tang¹•Institutions (1)

Nanjing University¹

01 May 2002-Artificial Intelligence

TL;DR: The bias-variance decomposition of the error is provided in this paper, which shows that the success of GASEN may lie in that it can significantly reduce the bias as well as the variance.

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1,898 citations

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Journal Article•DOI•

Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior

[...]

Shan He¹, Qinghua Wu¹, Jon R. Saunders¹•Institutions (1)

University of Liverpool¹

01 Oct 2009-IEEE Transactions on Evolutionary Computation

TL;DR: A novel optimization algorithm, group search optimizer (GSO), which is inspired by animal behavior, especially animal searching behavior, and has competitive performance to other EAs in terms of accuracy and convergence speed, especially on high-dimensional multimodal problems.

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Abstract: Nature-inspired optimization algorithms, notably evolutionary algorithms (EAs), have been widely used to solve various scientific and engineering problems because of to their simplicity and flexibility. Here we report a novel optimization algorithm, group search optimizer (GSO), which is inspired by animal behavior, especially animal searching behavior. The framework is mainly based on the producer-scrounger model, which assumes that group members search either for ldquofindingrdquo (producer) or for ldquojoiningrdquo (scrounger) opportunities. Based on this framework, concepts from animal searching behavior, e.g., animal scanning mechanisms, are employed metaphorically to design optimum searching strategies for solving continuous optimization problems. When tested against benchmark functions, in low and high dimensions, the GSO algorithm has competitive performance to other EAs in terms of accuracy and convergence speed, especially on high-dimensional multimodal problems. The GSO algorithm is also applied to train artificial neural networks. The promising results on three real-world benchmark problems show the applicability of GSO for problem solving.

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658 citations

Cites background or methods from "A Genetic Algorithm for Function Op..."

..., were set to be default as recommended in [40]....
[...]
...The selection function we used was normalized geometric ranking [40]....
[...]
...The GA toolbox we used in our experiments was the genetic algorithm optimization toolbox (GAOT) [40]....
[...]
...For the 300-dimensional cases, since there are very few results published at present, besides GAOT and PSOt we also implemented EP and ES for comparison....
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Proceedings Article•

Genetic CNN

[...]

Lingxi Xie¹, Alan L. Yuille¹•Institutions (1)

Johns Hopkins University¹

01 Oct 2017

TL;DR: Zhang et al. as mentioned in this paper proposed an encoding method to represent each network structure in a fixed-length binary string, which is initialized by generating a set of randomized individuals and defined standard genetic operations, e.g., selection, mutation and crossover, to generate competitive individuals and eliminate weak ones.

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Abstract: The deep convolutional neural network (CNN) is the state-of-the-art solution for large-scale visual recognition. Following some basic principles such as increasing network depth and constructing highway connections, researchers have manually designed a lot of fixed network architectures and verified their effectiveness.,,In this paper, we discuss the possibility of learning deep network structures automatically. Note that the number of possible network structures increases exponentially with the number of layers in the network, which motivates us to adopt the genetic algorithm to efficiently explore this large search space. The core idea is to propose an encoding method to represent each network structure in a fixed-length binary string. The genetic algorithm is initialized by generating a set of randomized individuals. In each generation, we define standard genetic operations, e.g., selection, mutation and crossover, to generate competitive individuals and eliminate weak ones. The competitiveness of each individual is defined as its recognition accuracy, which is obtained via a standalone training process on a reference dataset. We run the genetic process on CIFAR10, a small-scale dataset, demonstrating its ability to find high-quality structures which are little studied before. The learned powerful structures are also transferrable to the ILSVRC2012 dataset for large-scale visual recognition.

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551 citations

Proceedings Article•DOI•

PSOt - a particle swarm optimization toolbox for use with Matlab

[...]

B. Birge¹•Institutions (1)

North Carolina State University¹

24 Apr 2003

TL;DR: A particle swarm optimization toolbox for use with the Matlab scientific programming environment has been developed and PSO is introduced briefly and the use of the toolbox is explained with some examples.

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Abstract: A particle swarm optimization toolbox (PSOt) for use with the Matlab scientific programming environment has been developed. PSO is introduced briefly and then the use of the toolbox is explained with some examples. A link to downloadable code is provided.

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504 citations

Journal Article•DOI•

Gear fault detection using artificial neural networks and support vector machines with genetic algorithms

[...]

Biswanath Samanta¹•Institutions (1)

Sultan Qaboos University¹

01 May 2004-Mechanical Systems and Signal Processing

TL;DR: A study is presented to compare the performance of gear fault detection using artificial neural networks (ANNs) and support vector machines (SMVs) and for most of the cases considered, the classification accuracy of SVM is better than ANN, without GA.

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493 citations

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References

PDF

Open Access

More filters

Book•

Genetic algorithms in search, optimization, and machine learning

[...]

David E. Goldberg

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.

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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

...read moreread less

52,797 citations

Genetic algorithms in search, optimization and machine learning

[...]

David E. Goldberg

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.

...read moreread less

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.

...read moreread less

33,034 citations

"A Genetic Algorithm for Function Op..." refers background or methods in this paper

...Genetic algorithms have been used to solve di cult problems with objective functions that do not possess \nice" properties such as continuity, di erentiability, satisfaction of the Lipschitz Condition, etc.[Davis 1991; Goldberg 1989; Holland 1975; Michalewicz 1994]....
[...]
...A more complete discussion of genetic algorithms, including extensions and related topics, can be found in the books by Davis [Davis 1991], Goldberg [Goldberg 1989], Holland[Holland 1975], and Michalewicz [Michalewicz 1994]....
[...]
...There are several schemes for the selection process: roulette wheel selection and its extensions, scaling techniques, tournament, elitist models, and ranking methods [Goldberg 1989; Michalewicz 1994]....
[...]

Book•

Adaptation in natural and artificial systems

[...]

John H. Holland

01 Jan 1975

TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.

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Abstract: Name of founding work in the area. Adaptation is key to survival and evolution. Evolution implicitly optimizes organisims. AI wants to mimic biological optimization { Survival of the ttest { Exploration and exploitation { Niche nding { Robust across changing environments (Mammals v. Dinos) { Self-regulation,-repair and-reproduction 2 Artiicial Inteligence Some deenitions { "Making computers do what they do in the movies" { "Making computers do what humans (currently) do best" { "Giving computers common sense; letting them make simple deci-sions" (do as I want, not what I say) { "Anything too new to be pidgeonholed" Adaptation and modiication is root of intelligence Some (Non-GA) branches of AI: { Expert Systems (Rule based deduction)

...read moreread less

32,573 citations

"A Genetic Algorithm for Function Op..." refers background or methods in this paper

...Genetic algorithms have been used to solve di cult problems with objective functions that do not possess \nice" properties such as continuity, di erentiability, satisfaction of the Lipschitz Condition, etc.[Davis 1991; Goldberg 1989; Holland 1975; Michalewicz 1994]....
[...]
...In Holland's original design, the alphabet was limited to binary digits....
[...]
...Roulette wheel, developed by Holland [Holland 1975], was the rst selection method....
[...]
...A more complete discussion of genetic algorithms, including extensions and related topics, can be found in the books by Davis [Davis 1991], Goldberg [Goldberg 1989], Holland[Holland 1975], and Michalewicz [Michalewicz 1994]....
[...]
...The implicit parallelism is due to the schema theory developed by Holland, while the explicit parallelism arises from the manipulation of a population of points|the evaluation of the tness of these points is easy to accomplish in parallel....
[...]

Book•

Genetic Algorithms + Data Structures = Evolution Programs

[...]

Zbigniew Michalewicz

01 Jan 1992

TL;DR: GAs and Evolution Programs for Various Discrete Problems, a Hierarchy of Evolution Programs and Heuristics, and Conclusions.

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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.

...read moreread less

12,212 citations

Book•

Handbook of Genetic Algorithms

[...]

Lawrence Davis

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.

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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.

...read moreread less

6,758 citations

"A Genetic Algorithm for Function Op..." refers background or methods in this paper

...Genetic algorithms have been used to solve di cult problems with objective functions that do not possess \nice" properties such as continuity, di erentiability, satisfaction of the Lipschitz Condition, etc.[Davis 1991; Goldberg 1989; Holland 1975; Michalewicz 1994]....
[...]
...A more complete discussion of genetic algorithms, including extensions and related topics, can be found in the books by Davis [Davis 1991], Goldberg [Goldberg 1989], Holland[Holland 1975], and Michalewicz [Michalewicz 1994]....
[...]
...Many researchers have shown that GAs perform well for a global search but perform very poorly in a localized search [Davis 1991; Michalewicz 1994; Houck et al. 1995a; Bersini and Renders 1994]....
[...]