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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
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A Survey of Methods Available for the Numerical Optimization of Continuous Dynamic Systems
TL;DR: The advantages and disadvantages of recently developed methods, using evolutionary algorithms or metaheuristics, to solve similar parameter optimization problems and an attempt to answer the question of what is now the best extant numerical solution method.
A survey of multiobjective optimization in engineering design
TL;DR: A survey of techniques to conduct multiobjective optimization in an engineering design context is presented in this article, where the authors discuss some of the difficulties of expressing the value of a deign and how to characterize different design variables.
Journal ArticleDOI
An incremental genetic algorithm approach to multiprocessor scheduling
TL;DR: A genetic algorithm approach to the problem of task scheduling for multiprocessor systems that requires minimal problem specific information and no problem specific operators or repair mechanisms and is able to automatically adapt to changing targets.
Journal ArticleDOI
Hybridization of fuzzy GBML approaches for pattern classification problems
TL;DR: A hybrid algorithm of two fuzzy genetics-based machine learning approaches (i.e., Michigan and Pittsburgh) for designing fuzzy rule-based classification systems is proposed and shows that the hybrid algorithm has higher search ability.
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Application and comparison of metaheuristic techniques to generation expansion planning problem
TL;DR: This work presents both application and comparison of the metaheuristic techniques to generation expansion planning (GEP) problem and the effectiveness of each proposed methods has been illustrated in detail.
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