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A genetic algorithm with disruptive selection
IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 1996Genetic algorithms are a class of adaptive search techniques based on the principles of population genetics. The metaphor underlying genetic algorithms is that of natural evolution. Applying the "survival-of-the-fittest" principle, traditional genetic algorithms allocate more trials to above-average schemata.
Ting Kuo, Shu-Yuen Hwang
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Theriogenology, 2008
Selection of boars by visual appraisal is the simplest and oldest method used by the swine industry. However, individual performance testing, and later use of computers to incorporate relatives' data and account for environmental variation, resulted in greater rate of improvement for economically important traits.
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Selection of boars by visual appraisal is the simplest and oldest method used by the swine industry. However, individual performance testing, and later use of computers to incorporate relatives' data and account for environmental variation, resulted in greater rate of improvement for economically important traits.
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Genetic Selection for Mastitis Resistance
Veterinary Clinics of North America: Food Animal Practice, 2018Mastitis is a prevalent and costly disease on dairy farms. Improved management and hygiene can reduce the risk of infection by contagious or environmental pathogens, and genetic selection can confer permanent improvement in mastitis resistance. National veterinary recording systems in the Nordic countries have allowed direct selection for sire families
Kent A, Weigel, George E, Shook
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Some Models of Genetic Selection
Biometrics, 1979This paper begins with a description of the classical theory of viability selection in which probabilities that individuals of various genotypes survive are in proportions that do not change with time and are independent of population structure. Salient features of viability selection with one and two loci are reviewed.
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On the effect of selection in genetic algorithms
Random Structures and Algorithms, 2001Summary: To study the effect of selection with respect to mutation and mating in genetic algorithms, we consider two simplified examples in the infinite population limit. Both algorithms are modeled as measure valued dynamical systems and are designed to maximize a linear fitness on the half line. Thus, they both trivially converge to infinity.
Christian Mazza, Didier Piau
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Model selection in genetic programming
Proceedings of the 12th annual conference on Genetic and evolutionary computation, 2010In this paper we discuss the problem of model selection in Genetic Programming. We present empirical comparisons between classical statistical methods (AIC, BIC) adapted to Genetic Programming and the Structural Risk Minimization method (SRM) based on Vapnik-Chervonenkis theory (VC), for symbolic regression problems with added noise.
Cruz E. Borges +2 more
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Genetic interactions drive selection
Science, 2017Evolutionary Genetics Most individuals carry at least some potentially deleterious variants in their genome. But the effects of these mutations on individuals are not well understood. Sohail et al. examined loss-of-function (LOF) mutations in the genomes of humans and flies.
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Sexual Selection for Genetic Algorithms
Artificial Intelligence Review, 2003Genetic Algorithms (GA) have been widely used in operations research and optimization since first proposed. A typical GA comprises three stages, the encoding, the selection and the recombination stages. In this work, we focus our attention on the selection stage of GA, and review a few commonly employed selection schemes and their associated scaling ...
Kai Song Goh +2 more
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The driven genetic selection mechanism
SMC 2000 Conference Proceedings. 2000 IEEE International Conference on Systems, Man and Cybernetics. 'Cybernetics Evolving to Systems, Humans, Organizations, and their Complex Interactions' (Cat. No.00CH37166), 2002This paper defines a new distance measurement for binary strings called the extended Hamming distance. Using this measurement we prove that the binary decoding method in the simple genetic algorithm satisfies the demand of certain continuity. Based on this continuity, we propose a new selection mechanism called the driven selection mechanism.
Yongchuan Tang, Tianmin Huang
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Genetic engineering: Unnatural selection
Nature, 2003Evolving techniques for redesigning organisms have enormous potential but they must be matched with equally sophisticated methods for evaluating their benefits and risks.
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