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Distance Measures in Genetic Algorithms
2004Metric is one of the fundamental tools for understanding space. It gives induced topology to the space and it is the most basic way to provide the space with topology. Di.erent metrics make di.erent topologies. The shape of the space largely depends on its metric. In understanding genetic algorithms, metric is also basic and important.
Yong-Hyuk Kim, Byung Ro Moon
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The Statistical Properties of Genetic Absolute Distance
Biometrical Journal, 1986AbstractThe statistical properties of one estimator of absolute genetic distance (1/2) K∑i=1 |pxi‐yr|, tween two populations X and Y, are presented. It is shown that using this distance in small samples can be misleading particularly when populations are close to each other.
Katz, M., Goux, J.-M.
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Universal information distance for genetic programming
Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, 2014This paper presents a genotype-level distance metric for Genetic Programming (GP) based on the symmetric difference concept: first, the information contained in individuals is expressed as a set of symbols (the content of each node, its position inside the tree, and recurring parent-child structures); then, the difference between two individuals is ...
GAUDESI, MARCO +2 more
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Operator-Based Distance for Genetic Programming: Subtree Crossover Distance
2005This paper explores distance measures based on genetic operators for genetic programming using tree structures. The consistency between genetic operators and distance measures is a crucial point for analytical measures of problem difficulty, such as fitness distance correlation, and for measures of population diversity, such as entropy or variance. The
Steven M. Gustafson, Leonardo Vanneschi
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Behavior Genetics, 2011
Five possible mechanisms might underlie kin recognition in social groups: spatial location, familiarity through prior association, phenotype matching, recognition alleles, or rejecting unfamiliar cues. Kin recognition by phenotype matching relies on a strong correlation between genotype and phenotype.
Stephen J, Martin +3 more
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Five possible mechanisms might underlie kin recognition in social groups: spatial location, familiarity through prior association, phenotype matching, recognition alleles, or rejecting unfamiliar cues. Kin recognition by phenotype matching relies on a strong correlation between genotype and phenotype.
Stephen J, Martin +3 more
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An efficient distance metric for linear genetic programming
Proceedings of the 15th annual conference on Genetic and evolutionary computation, 2013Defining a distance measure over the individuals in the population of an Evolutionary Algorithm can be exploited for several applications, ranging from diversity preservation to balancing exploration and exploitation. When individuals are encoded as strings of bits or sets of real values, computing the distance between any two can be a straightforward ...
GAUDESI, MARCO +2 more
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Population genetics in the province of Ferrara. I. Genetic distances and geographic distances.
American journal of human genetics, 1977The ABO and Rh systems of the population in 26 residential units in the province of Ferrara were studied to detect the effect of genetic drift on the differentiation of gene frequencies. Results obtained from principal components analysis and dendrograms were similar. A significant correlation between genetic and geographic distance was found.
P, Zanardi +3 more
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Indicators of Genetic Distance
1974In recent years, considerable attention has been devoted to measuring the genetic distance between populations, and several indices of distance have been proposed and used.
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On measuring genetic distance by selection intensity
Annals of Human Genetics, 1978SummaryA simple selection model (fitness 1, w, w2 for AA, Aa, aa) is employed to change the gene fre‐ quency of one population to another. The genetic distance is then defined as d = ‐log w between the two populations. The distance is symmetrical with respect to the two populations. It is additive: d, = dI2+d23.
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