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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
A Strengthened Dominance Relation Considering Convergence and Diversity for Evolutionary Many-Objective Optimization
TL;DR: A novel dominance relation is proposed to better balance convergence and diversity for evolutionary many-objective optimization, where only the best converged candidate solution is identified to be nondominated in each niche.
Book ChapterDOI
The Niched Pareto Genetic Algorithm 2 Applied to the Design of Groundwater Remediation Systems
TL;DR: This work is a first attempt to apply EMO to the long-term remediation of contaminated water, and an improved version of the Niched Pareto GA (NPGA 2) is applied to determine the pumping rates for up to fifteen fixed-location wells.
Journal ArticleDOI
An information theoretic method for developing modular architectures using genetic algorithms
TL;DR: The design structure matrix (DSM) is used to visualize the product architecture and to develop the basic building blocks required for the identification of product modules, leading to a new clustering method based on the minimum description length (MDL) principle and a simple genetic algorithm.
Book ChapterDOI
Data-Driven Modelling: Concepts, Approaches and Experiences
TL;DR: This chapter reviews the main concepts and approaches of data-driven modelling, which is based on computational intelligence and machine-learning methods, and their combination via committee approaches.
Journal ArticleDOI
Exploration and Exploitation in the Presence of Network Externalities
Jongseok Lee,Jeho Lee,Habin Lee +2 more
TL;DR: Numerical analysis indicates that the exploration of a new, incompatible technology is more likely to increase the chance of firm growth when there are a substantial number of power users or when a new technology is introduced before an established technology takes off.
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