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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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Proceedings Article
Breeding Decision Trees Using Evolutionary Techniques
TL;DR: It is demonstrated that the derived hypotheses of standard algorithms can substantially deviate from the optimum and this deviation can be reduced using global metrics of tree quality like the one proposed.
Book ChapterDOI
Robust multi-objective optimization in high dimensional spaces
TL;DR: A new relation called ∈-Preferred is presented that extends existing approaches and clearly outperforms these for high dimensions and enhances the robustness significantly in MOO for high dimensional spaces.
Journal ArticleDOI
Robustness in multi-objective optimization using evolutionary algorithms
TL;DR: The combination preferred plus the same MOEA are used successfully to obtain the fittest and most robust Pareto optimal frontiers for a few more complex multi-criteria optimization problems.
Journal ArticleDOI
A model of scale effects in mammalian quadrupedal running
TL;DR: This work supports the idea that a single, integrative model can predict important features of running across size by employing simple strategies to control overall leg stiffness and provides a quantitative framework for testing hypotheses that relate limb control, stability and metabolic cost.
ReportDOI
A review on biomass densification technologie for energy application
TL;DR: In this article, the authors present an overview of the torrefaction process and do a literature review on the physical properties of torrefied raw material and torref reaction gas composition.
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