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Learning and Lineage Selection in Genetic Algorithms
Proceedings. IEEE SoutheastCon, 2005., 2005Lineage selection is a process by which traits that are not directly assessed by the fitness function can evolve. Reported here is an investigation of the effects of individual learning on the evolution of one such trait, self-adaptive mutation rates. It is found that the efficacy of the learning mechanism employed (its potential to increase individual
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Gene Selection Using Genetic Algorithms
2004Microarrays are emerging technologies that allow biologists to better understand the interactions between disease and normal states, at genes level. However, the amount of data generated by these tools becomes problematic when data are supposed to be automatically analyzed (e.g., for diagnostic purposes).
André Ponce de Leon F. de Carvalho +1 more
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Multi-Level Selection Genetic Algorithm applied to CEC '09 test instances
IEEE Congress on Evolutionary Computation, 2017P. Grudniewski, A. Sobey
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Research and Application of Hybrid Random Selection Genetic Algorithm
International Symposium on Computational Intelligence and Design, 2017Jin Zhu, Libo Huai, Rong-yi Cui
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A Genetic Algorithm for Selecting Horizontal Fragments
2009Decision support applications require complex queries, e.g., multi way joins defining on huge warehouses usually modelled using star schemas, i.e., a fact table and a set of data dimensions (Papadomanolakis & Ailamaki, 2004). Star schemas have an important property in terms of join operations between dimensions tables and the fact table (i.e., the ...
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Formal models of selection in genetic algorithms
1994In 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
Lorenza Saitta +2 more
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A new and fast rival genetic algorithm for feature selection
Journal of Supercomputing, 2020J. Too, A. Abdullah
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A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation, 2002K. Deb +3 more
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A Synergistic Selection Strategy in the Genetic Algorithms [PDF]
, 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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Feature selection based on hybridization of genetic algorithm and competitive swarm optimizer
Soft Computing - A Fusion of Foundations, Methodologies and Applications, 2020Ye Ding, Kui Zhou, Weihong Bi
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