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Genetic Algorithms
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The article was published on 2002-01-01 and is currently open access. It has received 17039 citations till now.read more
Citations
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Journal ArticleDOI
Object detection using feature subset selection
TL;DR: It is argued that feature selection is an important problem in object detection and demonstrated that genetic algorithms (GAs) provide a simple, general, and powerful framework for selecting good subsets of features, leading to improved detection rates.
Computational intelligence based on the behavior of cats
Shu-Chuan Chu,Pei-Wei Tsai +1 more
TL;DR: A new optimization algorithm, namely, Cat Swarm Optimization (CSO) is proposed, which is generated by observing the behavior of cats, and composed of two sub-models by simulating thebehavior of cats.
Journal ArticleDOI
Statistical Improvement Criteria for Use in Multiobjective Design Optimization
TL;DR: The work presented combines design of experiment methods with kriging (Gaussian process) models to enable the parallel evolution of multiobjective Pareto sets through the use of updating schemes based on new extensions of the expected improvement criterion commonly applied in single-objective searches.
Journal ArticleDOI
Optimal division of data for neural network models in water resources applications
TL;DR: In this paper, a case study is presented in which ANN models developed using each data division technique are used to forecast salinity in the River Murray at Murray Bridge (South Australia) 14 days in advance.
Proceedings ArticleDOI
How to effectively use topic models for software engineering tasks? an approach based on genetic algorithms
Annibale Panichella,Bogdan Dit,Rocco Oliveto,Massimilano Di Penta,Denys Poshynanyk,Andrea De Lucia +5 more
TL;DR: A novel solution to adapt, configure and effectively use a topic modeling technique, namely Latent Dirichlet Allocation (LDA), to achieve better (acceptable) performance across various SE tasks is proposed.
References
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Book
Genetic algorithms in search, optimization, and machine learning
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.
Book
Genetic Algorithms + Data Structures = Evolution Programs
TL;DR: GAs and Evolution Programs for Various Discrete Problems, a Hierarchy of Evolution Programs and Heuristics, and Conclusions.
Journal ArticleDOI
An Introduction to Genetic Algorithms.
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.
Book
Handbook of Genetic Algorithms
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.
Journal ArticleDOI
An Introduction to Population Genetics Theory
James F. Crow,Motoo Kimura +1 more
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