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A hybrid intelligent genetic algorithm
Advanced Engineering Informatics, 2005Application of genetic algorithms to optimization of complex problems can lead to a substantial computational effort as a result of the repeated evaluation of the objective function(s) and the population-based nature of the search. This is often the case where the objective function evaluation is costly, for example, when the value is obtained ...
A.A. Javadi, R. Farmani, T.P. Tan
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Rice Artificial Hybridization for Genetic Analysis
2012Artificial hybridization has probably been practiced since ancient time; however, the science of genetics did not initiate until Gregor Mendel conducted a series of crosses between different pure lines of garden pea and made careful observations and systematical analyses of their offspring.
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Biology and Genetics of Hybrid Resistance
1987Publisher Summary The first complication of bone marrow transplantation noted was “homologous or secondary disease,” which usually occurs after the early recovery of hemopoietic cells and is because of a graft-versus-host reaction. In that disease, there is extensive proliferation of histiocytes and epithelioid cells, infiltration of various organs ...
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Hybrid Two-Population Genetic Algorithm
2001Genetic Algorithms are non-deterministic, stochastic-search adaptive methods which use the theories of natural evolution and selection in order to solve a problem within a complex range of possible solutions. The aim is to control the distribution of the search space by incorporating an exhaustive method in order to maintain a constant evolution of the
Julian Dorado +4 more
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Genetic Analysis of Hybrid Zones
1993Abstract When two distinct gene pools meet and produce fertile hybrids, the outcome varies from gene to gene. At some loci a universally favorable allele has been established on one side. Such alleles soon spread through the whole population and hence differences are rarely observed.
Nicholas H Barton, Katherine S Gale
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Hybrid genetic algorithms for timetabling
International Journal of Intelligent Systems, 1996Summary: Hybrid genetic algorithms are presented that use optimization heuristics and genetic techniques to outperform all existing programs for the timetabling problem. The time-tabling problem is very hard (NP-complete) and a general polynomial time deterministic algorithm is not known.
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