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Genetic variability and associations of Trakehner and other horse populations in Lithuania. [PDF]
Arch Anim BreedRačkauskaitė A +4 more
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Genetic Selection for Growth Rate Reshapes the Plasma Metabolome of Rabbit Does Derived from Vitrified Embryos: Insights into Nutrient Metabolism and Productive Efficiency. [PDF]
Vet SciMateo-López J +9 more
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Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2021
This paper proposes an innovative selection operator based on concepts from quantum mechanics. In particular, a quantum state is used to embody genetic individuals and their fitness values, and a quantum algorithm known as amplitude amplification is used to modify this state in order to create a quantum superposition in which the probability to measure
Acampora G. +2 more
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This paper proposes an innovative selection operator based on concepts from quantum mechanics. In particular, a quantum state is used to embody genetic individuals and their fitness values, and a quantum algorithm known as amplitude amplification is used to modify this state in order to create a quantum superposition in which the probability to measure
Acampora G. +2 more
openaire +2 more sources
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.
openaire +2 more sources
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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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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