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Evolutionary algorithms in theory and practice
TLDR
In this work, the author compares the three most prominent representatives of evolutionary algorithms: genetic algorithms, evolution strategies, and evolutionary programming within a unified framework, thereby clarifying the similarities and differences of these methods.About:
The article was published on 1996-01-01 and is currently open access. It has received 2679 citations till now. The article focuses on the topics: Evolutionary music & Evolutionary programming.read more
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Journal ArticleDOI
A genetic algorithm with adaptive mutations and family competition for training neural networks.
TL;DR: A new evolutionary technique to train three general neural networks based on family competition principles and adaptive rules is presented, which combines decreasing-based mutations and self-adaptive mutations to collaborate with each other.
Journal ArticleDOI
Manifold regularized stacked denoising autoencoders with feature selection
TL;DR: Findings from this study can be used as effective guideline in learning both the structure and parameters of deep neural networks (DNNs) with manifold regularization and feature selection techniques.
Proceedings ArticleDOI
Local Learning and Search in Memetic Algorithms
Frederico Gadelha Guimarães,Elizabeth F. Wanner,Felipe Campelo,Ricardo H. C. Takahashi,Hajime Igarashi,David A. Lowther,Jaime A. Ramírez +6 more
TL;DR: This work proposes the local learning of the objective and constraint functions prior to the local search phase of memetic algorithms, based on the samples gathered by the population through the evolutionary process, over an approximated model.
Journal ArticleDOI
An enhanced approach for shape optimization using an adaptive algorithm
TL;DR: An insight into the framework of evolutionary algorithms is given and it is shown that heuristic techniques yield satisfiable results within reasonable limits of the number of function evaluations.
Proceedings ArticleDOI
A genetic algorithm for graph coloring using single parent conflict gene crossover and mutation with conflict gene removal procedure
TL;DR: An improved genetic method which uses the single parent conflict-Gene crossover and conflict-gene mutation operators along with the conflict- gene removal procedure to solve the graph coloring problem is exhibited.