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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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ReportDOI
On the Virtues of Parameterized Uniform Crossover
TL;DR: This paper attempts to reconcile opposing views of uniform crossover and present a framework for understanding its virtues, as a growing body of experimental evidence suggests otherwise.
Journal ArticleDOI
The gambler's ruin problem, genetic algorithms, and the sizing of populations
TL;DR: A model to predict the convergence quality of genetic algorithms based on the size of the population based on an analogy between selection in GAs and one-dimensional random walks is presented.
Journal ArticleDOI
Genetic algorithms, selection schemes, and the varying effects of noise
Brad L. Miller,David E. Goldberg +1 more
TL;DR: Models for several selection schemes are developed that successfully predict the convergence characteristics of GAs within noisy environments that include proportionate selection, tournament selection, (, ) selection, and linear ranking selection.