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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.

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Optimal chiller loading by genetic algorithm for reducing energy consumption

TL;DR: This study employs genetic algorithm (GA) to solve optimal chiller loading (OCL) problem by using the part load ratios (PLR) of chiller units to binary code chromosomes, and executing reproduction, crossover and mutation operation.
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A regularity model-based multiobjective estimation of distribution algorithm with reducing redundant cluster operator

TL;DR: This paper improves the clustering process and proposes a reducing redundant cluster operator (RRCO) to build more precise model during the evolution of RM-MEDA, and presents an improved version of the algorithm, named IRM-medA, which outperforms RM- MEDA in terms of efficiency and effectiveness.
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Restoration of Bridge Networks after an Earthquake: Multicriteria Intervention Optimization

TL;DR: In this paper, an optimization procedure for the restoration activities associated with the bridges of a transportation network severely damaged by an earthquake is presented, where the goal is to maximize the network resilience, minimize the time required to reach a target functionality level, and minimize the total cost of restoration activities.
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Survey of Intelligent Control Techniques for Humanoid Robots

TL;DR: This paper represents an attempt to cover the basic principles and concepts of intelligent control in humanoid robotics, with an outline of a number of recent algorithms used in advanced control of humanoid robots.
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Genetics-Based Machine Learning for Rule Induction: State of the Art, Taxonomy, and Comparative Study

TL;DR: This paper has a double aim: to present a taxonomy of the genetics-based machine learning approaches for rule induction, and to develop an empirical analysis both for standard classification and for classification with imbalanced data sets.
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, +1 more
- 01 Sep 1971 - 
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