Open AccessBook
Genetic Algorithms
About:
The article was published on 2002-01-01 and is currently open access. It has received 17039 citations till now.read more
Citations
More filters
Journal ArticleDOI
Stochastic Optimization: a Review
Dimitris Fouskakis,David Draper +1 more
TL;DR: In this paper, the authors review three leading stochastic optimization methods (simulated annealing, genetic algorithms, and tabu search) for variable selection in generalized linear models.
Dissertation
Robust non-linear control through neuroevolution
TL;DR: This dissertation develops a methodology for solving real world control tasks consisting of an efficient neuroevolution algorithm that solves difficult non-linear control tasks by coevolving neurons, an incremental evolution method to scale the algorithm to the most challenging tasks, and a technique for making controllers robust so that they can transfer from simulation to the real world.
Journal ArticleDOI
Smart Pareto filter: obtaining a minimal representation of multiobjective design space
TL;DR: Smart Pareto sets as mentioned in this paper are a general approach to solving multiobjective optimization problems, which is based on generating a set of optimal solutions and then selecting the most attractive solution from this set as the final design.
Journal ArticleDOI
Intelligent feature selection and classification techniques for intrusion detection in networks: a survey
Sannasi Ganapathy,K. Kulothungan,S. Muthurajkumar,Muthusamy Vijayalakshmi,P. Yogesh,Arputharaj Kannan +5 more
TL;DR: A survey on intelligent techniques for feature selection and classification for intrusion detection in networks based on intelligent software agents, neural networks, genetic algorithms, neuro-genetic algorithms, fuzzy techniques, rough sets, and particle swarm intelligence is proposed.
Journal ArticleDOI
A diversified multiobjective GA for optimizing reservoir rule curves
TL;DR: Results of this work indicate that the proposed macro-evolutionary multiobjective genetic algorithm (MMGA) is highly competitive and provides a viable alternative to solve multiobjectives optimization problems for water resources planning and management.
References
More filters
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