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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 Article
A simple genetic algorithm for multiple sequence alignment.
Cedric Gondro,Brian Kinghorn +1 more
TL;DR: The results show that a simple genetic algorithm is capable of optimizing an alignment without the need of the excessively complex operators used in prior study.
Journal ArticleDOI
A binary ABC algorithm based on advanced similarity scheme for feature selection
TL;DR: A binary artificial bee colony (ABC) algorithm for the feature selection problems is proposed by integrating evolutionary based similarity search mechanisms into an existing binary ABC variant, which can eliminate irrelevant and redundant features more effectively than the other approaches.
Proceedings ArticleDOI
sFuzz: an efficient adaptive fuzzer for solidity smart contracts
TL;DR: SFuzz as discussed by the authors combines the strategy in the AFL fuzzer and an efficient lightweight multi-objective adaptive strategy targeting those hard-to-cover branches, and has been applied to more than 4 thousand smart contracts.
Journal ArticleDOI
Genetic Algorithm Based Demand Side Management for Smart Grid
TL;DR: The main objective is to minimize the power utilization during the electricity rush hour by effectively distributing the power available during off-peak hour by using Genetic Algorithm in Demand Side Management (GA-DSM).
Journal ArticleDOI
The basis risk of catastrophic-loss index securities
TL;DR: In this paper, the authors analyzed the effectiveness of catastrophic loss index options in hedging hurricane losses for Florida insurers and found that insurers in the two largest size quartiles can hedge losses almost as effectively using contracts based on four intrastate indices as they can using contracts that settle on their own losses.
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