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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
Deep learning in neural networks
TL;DR: This historical survey compactly summarizes relevant work, much of it from the previous millennium, review deep supervised learning, unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
Journal ArticleDOI
Grey Wolf Optimizer
TL;DR: The results of the classical engineering design problems and real application prove that the proposed GWO algorithm is applicable to challenging problems with unknown search spaces.
Journal ArticleDOI
The Whale Optimization Algorithm
Seyedali Mirjalili,Andrew Lewis +1 more
TL;DR: Optimization results prove that the WOA algorithm is very competitive compared to the state-of-art meta-heuristic algorithms as well as conventional methods.
Proceedings ArticleDOI
Cuckoo Search via Lévy flights
Xin-She Yang,Suash Deb +1 more
TL;DR: A new meta-heuristic algorithm, called Cuckoo Search (CS), is formulated, based on the obligate brood parasitic behaviour of some cuckoo species in combination with the Lévy flight behaviour ofSome birds and fruit flies, for solving optimization problems.
Book
Nature-Inspired Metaheuristic Algorithms
TL;DR: This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search, and firefly algorithms.
References
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Book
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
TL;DR: The Design of Innovation as mentioned in this paper is a comprehensive step-by-step guide to designing and implementing competent genetic algorithms that solve hard problems quickly, reliably, and accurately, and how the invention of competent genetic algorithm amounts to the creation of an effective computational theory of human innovation.
Journal Article
Genetic Algorithms, Tournament Selection, and the Effects of Noise.
Brad L. Miller,David E. Goldberg +1 more
TL;DR: The model is shown to accurately predict the convergence ra te of a GA using tournament select ion in the onemax domain for a wide range of t ournament sizes and noise levels.
Proceedings Article
A study of permutation crossover operators on the traveling salesman problem
TL;DR: In this paper, three permutation crossovers are analyzed to characterize how they sample the o-schema space, and hence what type of problems they may be applicable to.
Journal Article
An overview of genetic algorithms: Part 1, fundamentals
TL;DR: Genetic Algorithms (GAs) are adaptive methods which may be used to solve search and optimisation problems based on the genetic processes of biological organisms, which simulate those processes in natural populations which are essential to evolution.
Book
Efficient and Accurate Parallel Genetic Algorithms
TL;DR: The Gambler's Ruin and Population Sizing is illustrated by a comparison of Master-Slave Parallel GAs with Markov Chain Models of Multiple Demes.