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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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Journal ArticleDOI
Hybrid evolutionary techniques for the maintenance scheduling problem
Edmund K. Burke,A.J. Smith +1 more
TL;DR: This paper investigates the use of a memetic algorithm for the thermal generator maintenance scheduling problem and concludes that the most effective method is a Memetic approach that employs a tabu-search operator.
Journal ArticleDOI
Learning with case-injected genetic algorithms
TL;DR: Results indicate that this GA-based machine-learning system learns to take less time to provide quality solutions to a new problem as it gains experience from solving other similar problems in design and optimization.
Journal ArticleDOI
Scalability problems of simple genetic algorithms
TL;DR: It is shown how the need for mixing places a boundary in the GA parameter space that delimits the region where the GA converges reliably to the optimum in problems of bounded difficulty unless the building blocks are tightly linked in the problem coding structure.
Proceedings ArticleDOI
Elitist recombination: an integrated selection recombination GA
Dirk Thierens,David E. Goldberg +1 more
TL;DR: An analytical model for optimizing the bit counting function is derived, and results suggest that elitist recombination is less sensitive to undersized populations, while there is no need to choose a specific value for the crossover probability.
Proceedings ArticleDOI
Domino convergence, drift, and the temporal-salience structure of problems
TL;DR: In this paper, the authors use a sequential parameterization approach to build models of the differential convergence behavior, and derive time complexities for the boundary case which is obtained with an exponentially scaled problem (BinInt).