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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
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Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves
TL;DR: Estimating the compressive strength of silica fume concrete using the ANN method was considered as a two-objective optimization problem, and an ANN model with just one hidden layer with five neurons and the Pearson correlation coefficient of 0.9617 was chosen as the final ANN model.
Journal ArticleDOI
Real-Parameter Evolutionary Monte Carlo With Applications to Bayesian Mixture Models
Faming Liang,Wing Hung Wong +1 more
TL;DR: An evolutionary Monte Carlo algorithm to sample from a target distribution with real-valued parameters is proposed and it is confirmed that the real-coded evolutionary algorithm is a promising general approach for simulation and optimization.
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An Evolutionary Multiobjective Approach for Community Discovery in Dynamic Networks
Francesco Folino,Clara Pizzuti +1 more
TL;DR: The detection of communities with temporal smoothness is formulated as a multiobjective problem and a method based on genetic algorithms is proposed and the main advantage of the algorithm is that it automatically provides a solution representing the best trade-off between the accuracy of the clustering obtained, and the deviation from one time step to the successive.
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Max-min surrogate-assisted evolutionary algorithm for robust design
TL;DR: This paper presents a novel evolutionary algorithm based on the combination of a max-min optimization strategy with a Baldwinian trust-region framework employing local surrogate models for reducing the computational cost associated with robust design problems.
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Barnacles Mating Optimizer: A new bio-inspired algorithm for solving engineering optimization problems
TL;DR: A novel bio-inspired optimization algorithm namely the Barnacles Mating Optimizer (BMO) algorithm to solve optimization problems that mimics the mating behaviour of barnacles in nature for solving optimization problems.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Book
Genetic Algorithms + Data Structures = Evolution Programs
TL;DR: GAs and Evolution Programs for Various Discrete Problems, a Hierarchy of Evolution Programs and Heuristics, and Conclusions.
Journal ArticleDOI
An Introduction to Genetic Algorithms.
TL;DR: An Introduction to Genetic Algorithms as discussed by the authors is one of the rare examples of a book in which every single page is worth reading, and the author, Melanie Mitchell, manages to describe in depth many fascinating examples as well as important theoretical issues.
Book
Handbook of Genetic Algorithms
TL;DR: This book sets out to explain what genetic algorithms are and how they can be used to solve real-world problems, and introduces the fundamental genetic algorithm (GA), and shows how the basic technique may be applied to a very simple numerical optimisation problem.
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An Introduction to Population Genetics Theory
James F. Crow,Motoo Kimura +1 more
TL;DR: An introduction to population genetics theory, An introduction to Population Genetics Theory, Population Genetics theory, Population genetics theory as discussed by the authors, Population genetics, population genetics, and population genetics theories, Population Genetic Theory