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GENETIC DIVERGENCE AND HYBRID SPECIATION
Evolution, 2007Although the evolutionary importance of natural hybridization has been debated for decades, it has become increasingly clear that hybridization plays a fundamental role in the evolution of many plant and animal taxa, sometimes resulting in the formation of entirely new species.
Chapman, M.A., Burke, J.M.
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A simplex genetic algorithm hybrid
Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97), 2002One of the main obstacles in applying genetic algorithms (GAs) to complex problems has been the high computational cost due to their slow convergence rate. To alleviate this difficulty, we developed a hybrid approach that combines a GA with a stochastic variant of the simplex method in function optimization. Our motivation for developing the stochastic
John Yen, Bogju Lee
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Genetic Mapping in Hybrid Zones
The American Naturalist, 2002Recent advances in genetic mapping methodologies make it feasible to localize quantitative trait loci (QTL) that contribute to adaptation and speciation. However, it has not been possible to employ these methods in many wild species because of difficulties associated with creating and propagating recombinant populations of sufficient size for QTL ...
Loren H, Rieseberg, C Alex, Buerkle
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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 ...
Akbar A. Javadi +2 more
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Hybrid parallelization of a compact genetic algorithm
Eleventh Euromicro Conference on Parallel, Distributed and Network-Based Processing, 2003. Proceedings., 2003Genetic Algorithms (GAs) are stochastic optimization heuristics in which searches in solution space are carried out by imitating the population genetics stated in Darwin's theory of evolution. We have focused this work on compact Genetic Algorithms (cGAs), which unlike standard GAs do not manage a population of solutions but only mimics its existence ...
José Ignacio Hidalgo +5 more
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MEmory Genetic Algorithm Hybridized for Zeolites
2017 IEEE Congress on Evolutionary Computation (CEC), 2017Zeolite structure determination is an interesting challenge even with the progress in terms of structural resolution from X-rays and electron diffraction. The infinite number of potential solutions and the computational cost of this problem make the use of an evolutionary algorithm significant for this challenge.
Abdelkafi, Omar +3 more
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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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A Hybrid Fuzzy-Genetic Algorithm
2006In this paper we present a hybrid fuzzy-genetic algorithm for the feature and instance subset selection problem. This algorithm combines a hybrid meta-heuristic algorithm and a fuzzy self-adaptive genetic algorithm with a rotary circular crossover which is based on a half uniform crossover.
Agustín León-Barranco +2 more
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Surrogate Genetics: The Use of Bacterial Hybrids as a Genetic Tool
Methods, 2000Experimental dissection of bacterial genomes requires a well-developed set of genetic tools, but many bacteria lack the essential tools required for genetic analysis. Recombination of a region of chromosomal DNA from poorly characterized donor bacteria with the chromosome of a suitable surrogate host creates a genetically malleable hybrid, providing a ...
S, Maloy, T, Zahrt
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The GA-P: a genetic algorithm and genetic programming hybrid
IEEE Expert, 1995The GA-P performs symbolic regression by combining the traditional genetic algorithms function optimization strength with the genetic-programming paradigm to evolve complex mathematical expressions capable of handling numeric and symbolic data. This technique should provide new insights into poorly understood data relationships. >
Les M. Howard, Donna J. D'Angelo
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