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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
Application of GA based optimal integral gains in fuzzy based active power-frequency control of non-reheat and reheat thermal generating systems
TL;DR: Results of comparative study show much improvement of transient responses in terms of settling times, undershoots, overshoots and df/dt in favor of GA based gains for non-reheat systems, and to get better optimal performance GA based optimal gains have also been determined for three-area system.
Proceedings ArticleDOI
Simultaneous use of different scalarizing functions in MOEA/D
TL;DR: An idea of using different types of scalarizing functions simultaneously, both the weighted Tchebycheff (Chebyshev) and the weighted sum are used for fitness evaluation and two methods for implementing this idea are examined.
Journal ArticleDOI
A real-coding jumping gene genetic algorithm (RJGGA) for multiobjective optimization
TL;DR: It has been observed that RJGGA is able to generate non-dominated solutions with a wider spread along the Pareto-optimal front and better address the issues regarding convergence and diversity in multiobjective optimization.
Proceedings ArticleDOI
Industrial applications
TL;DR: This first technical session is further developing the technological dimensions of technology transfer, with three illustrations of successful and representative industrial applications of embedded autonomy in spacecraft applications, advanced avionics solutions for satellite communication and component hybridization for navigation.
Journal ArticleDOI
Fuzzy model predictive control of non-linear processes using genetic algorithms
TL;DR: A new fuzzy control technique, which belongs to the popular family of control algorithms, called Model Predictive Controllers, which minimizes the difference between the model predictions and the desired trajectory over the prediction horizon and the control energy over a shorter control horizon.
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