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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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Numerical maximum likelihood estimation for the g-and-k and generalized g-and-h distributions
G. D. Rayner,Helen MacGillivray +1 more
TL;DR: Results indicate that sample sizes significantly larger than 100 should be used to obtain reliable estimates through maximum likelihood, and the appropriateness of using asymptotic methods examined.
Journal ArticleDOI
Optimal design of reliable network systems in presence of uncertainty
TL;DR: A multiple-objective optimization approach aimed at maximizing the network reliability estimate, and minimizing its associated variance when component types, with uncertain reliability, and redundancy levels are the decision variables is formulates.
Journal ArticleDOI
Gene Network Inference via Structural Equation Modeling in Genetical Genomics Experiments
TL;DR: The goal is gene network inference in genetical genomics or systems genetics experiments by constructing an encompassing directed network (EDN) and proposing structural equation modeling (SEM), because it can model cyclic networks and the EDN indeed contains feedback relationships.
Journal ArticleDOI
Determining optimum Genetic Algorithm parameters for scheduling the manufacturing and assembly of complex products
TL;DR: The overall objective is to use the most efficient Genetic Algorithm parameters that achieve minimum total costs and minimum spread, to solve a very large scheduling problem that is computationally expensive.
Journal ArticleDOI
Implementation of holistic water resources-economic optimization models for river basin management - Reflective experiences
TL;DR: This paper addresses the difficulties involved in large-scale holistic modeling for integrated river basin management, and provides solution methods taking a self-reflective stance through a prototype model.
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.
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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