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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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Analysis of Mixing in Genetic Algorithms: A Survey
Kumara Sastry,David E. Goldberg +1 more
TL;DR: A classification of the literature based on the role of recombination operators assumed by studies on one or more aspects of mixing is developed and provides a foundation for future research in understanding mixing in genetic algorithms.
Proceedings ArticleDOI
The response to selection equation for skew fitness distributions
TL;DR: The classical analysis is extended to skew fitness distributions and it is shown that, for a small number of variables, the Gamma distribution fits the distribution of the fitness values better than a normal distribution.
Book ChapterDOI
Scalability of selectorecombinative genetic algorithms for problems with tight linkage
Kumara Sastry,David E. Goldberg +1 more
TL;DR: Facetwise models are developed to predict the BB mixing time and the population sizing dictated by BB mixing for single-point crossover and suggest that for moderate-to-large problems, BB mixing bounds the population size required to obtain a solution of constant quality.