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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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Automated Operator Selection on Genetic Algorithms
2005Genetic Algorithms (GAs) have proven to be a useful means of finding optimal or near optimal solutions to hard problems that are difficult to solve by other means. However, determining which crossover and mutation operator is best to use for a specific problem can be a complex task requiring much trial and error. Furthermore, different operators may be
Fredrik G. Hilding, Koren Ward
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A genetic algorithm for feature selection in gait analysis
2016 IEEE Congress on Evolutionary Computation (CEC), 2016This paper deals with the opportunity of extracting useful information from medical data retrieved directly from a stereophotogrammetric system applied to gait analysis, which aims at controlling movements of patients affected by neurological diseases. The proposed approach is intended to a feature selection procedure as an optimization strategy based ...
ALTILIO, ROSA +4 more
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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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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 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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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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Automatic Feature Selection by Genetic Algorithms
2001The efficient and automatic selection of features from an initial raw data set is an optimization task met in numerous applications fields, e.g., multivariate data classification, analysis, and visualization. The reduction of the variable number reduces the detrimental effects of the well-known curse of dimensionality.
Michael Eberhardt +2 more
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Genetic testing in prostate cancer management: Considerations informing primary care
Ca-A Cancer Journal for Clinicians, 2022Veda N Giri +2 more
exaly
Current treatment and recent progress in gastric cancer
Ca-A Cancer Journal for Clinicians, 2021Smita S Joshi, Brian D Badgwell
exaly