Results 231 to 240 of about 228,099 (265)

Formal models of selection in genetic algorithms

1994
In this paper three formal models of selection operators (two known from the literature and one newly porposed) for genetic algorithms, used to learn structured concepts descriptions containing small disjuncts, are presented. The evolution of a population, according to these operators, with a generation gap equal to or less than one, is investigated in
Attilio Giordana, Filippo Neri, Lorenza Saitta   +2 more
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GrC model in Genetic Algorithm: Artificial Selection Algorithm

2008 IEEE International Conference on Granular Computing, 2008
Genetic Algorithm (GA), a programming technique that mimics natural evolution as a problem-solving strategy, has become popular since its appearance. It keeps the properties similar to natural selection systems. Many improved GAs has been proposed, however, natural selection essence is not changed.
Zehua Chen 0001, Gaowei Yan, Gang Xie 0001, Keming Xie   +3 more
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Entropy-Boltzmann selection in the genetic algorithms

IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 2003
A new selection method, entropy-Boltzmann selection, for genetic algorithms (GAs) is proposed. This selection method is based on entropy and importance sampling methods in Monte Carlo simulation. It naturally leads to adaptive fitness in which the fitness function does not stay fixed but varies with the environment.
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On the Influence of Selection Schemes on the Genetic Diversity in Genetic Algorithms

2009
This paper discusses some aspects of the general convergence behavior of genetic algorithms. Careful attention is given to how different selection strategies influence the progress of genetic diversity in populations. For being able to observe genetic diversity over time measures are introduced for estimating pairwise similarities as well as ...
Michael Affenzeller, Stephan M. Winkler, Andreas Beham, Stefan Wagner 0002   +3 more
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Parallel genetic algorithm with fading selection

International Journal of Computer Applications in Technology, 2014
This work presents a novel approach to designing the parallel genetic algorithm GA with fading selection for the solving of the problem of the shareholder value maximisation of an oil company. The algorithm based on the dynamical interaction of synchronised processes, which are interdependent GAs having own separate evolutions of their populations. The
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Genetic Algorithm as an Attributes Selection Tool for Learning Algorithms

2004
Learning algorithms, as NN or C4.5 require adequate sets of examples. In the paper we present the usability of genetic algorithms for selection significant features. Fitness of individuals is calculated on the basis of classification quality using NN or C4.5 algorithm.
Halina Kwasnicka, Piotr Orski
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Genetic algorithms in feature selection

IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028), 2003
We use a genetic algorithm (GA) for the feature selection problem. The method explores the space of possible subsets to obtain the set of features that maximizes the predictive accuracy and minimizes irrelevant attributes. We introduce a multiple correlation in a fitness function used by the GA to evaluate the fitness of each feature subset regarding ...
N. Chaikla, null Yulu Qi
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Genetic Algorithms and Model Selection

2017
With the rise of competition in the market, enhancing the marketing strategies has become the main interest of business owners to increase their revenues. And in order to achieve that, the main factor is to have science-based tactics like the ones provided by MassTer, the MMM software that uses advanced algorithms to estimate marketing mix models such ...
Ayari, Amani, Sayadi, Mounir
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A Synergistic Selection Strategy in the Genetic Algorithms

2006
According to the Neo-Darwinist, natural selection can be classified into three categories: directional selection, disruptive selection, and stabilizing selection. Traditional genetic algorithms can be viewed as a process of evolution based on directional selection that gives more chances of reproduction to superior individuals.
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Selection of Representatives Using Genetic Algorithms

2022
Yoshihiko Hamamoto, Y. Tsuneta, C. Kaneyama, Shingo Tomita   +3 more
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