Open Access
A Genetic Algorithm for Function Optimization: A Matlab Implementation
Christopher R. Houck
- 01 Jan 2001
1.3K
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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Citations
Ensembling neural networks: many could be better than all
Zhi-Hua Zhou,Jianxin Wu,Wei Tang +2 more
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.
2.2K
Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior
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.
728
Gear fault detection using artificial neural networks and support vector machines with genetic algorithms
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.
566
PSOt - a particle swarm optimization toolbox for use with Matlab
B. Birge
- 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.
556
•Proceedings Article
Genetic CNN
Lingxi Xie,Alan L. Yuille +1 more
- 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.
553
References
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.
58.6K
•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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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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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.
13.5K
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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.
8.2K
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