Results 281 to 290 of about 199,526 (313)
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Selective breeding in a multiobjective genetic algorithm
1998This paper describes an investigation of the efficacy of various elitist selection strategies in a multiobjective Genetic Algorithm implementation, with parents being selected both from the current population and from the archive record of nondominated solutions encountered during search.
I. Miller, Geoffrey T. Parks
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Genetic algorithm attributes for component selection
Research in Engineering Design, 1996This paper uses a genetic algorithm for component selection given a user-defined system layout, a database of components, and a defined set of design specifications. A genetic algorithm is a search method based on the principles of natural selection. An introduction to genetic algorithms is presented, and genetic algorithm attributes that are useful ...
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Vendor selection using genetic algorithm
The 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligence Systems, 2012Selecting the right vendor is a complex business decision due to a huge number of competing vendors with a large number of complex criteria. The organization will suffer in the long run if vendors are not chosen wisely. Under multi criteria decision making, an algorithm, named VSFI, based on fuzzy clustering was proposed to select the most optimal ...
Shaila Sharmeen +4 more
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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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Genetic algorithms in controller structure selection
1st International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications (GALESIA), 1995The selection of an appropriate set of manipulated variables to control a set of specified outputs is an important aspect of MIMO system design. This paper details how genetic algorithms may be used to provide an automated optimisation procedure for the selection of the input-output pairings based upon the Relative Gain Array (RGA).
I.G. French, C.S. Cox, C. K. S. Ho
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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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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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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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