Open AccessBook
Genetic Algorithms
About:
The article was published on 2002-01-01 and is currently open access. It has received 17039 citations till now.read more
Citations
More filters
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
More filters
Proceedings ArticleDOI
Fitness inheritance in genetic algorithms
TL;DR: An application to a GA-easy problem shows that greater efficiency can be obtained by evaluating only a small portion of the population, and a real-world search problem confirms these results.
Proceedings Article
Optimizing global-local search hybrids
TL;DR: This paper develops a framework for optimizing global-local hybrids of search or optimization procedures and derives a two-basin optimality criterion, which appears to be useful immediately in better understanding the economy of effective hybridization.
Journal ArticleDOI
A Seeded Memetic Algorithm for Large Unit Commitment Problems
Jorge Valenzuela,Alice E. Smith +1 more
TL;DR: The paper shows that the use of a memetic algorithm (MA), a genetic algorithm (GA) combined with local search, synergistically combined with Lagrangian relaxation is effective and efficient for solving large unit commitment problems in electric power systems.
Proceedings ArticleDOI
The Gene Expression Messy Genetic Algorithm
TL;DR: The Gene Expression Messy Genetic Algorithm (GEMGA)-a new generation of messy genetic algorithms that directly search for relations among the members of the search space, that emphasizes the role of gene expression.