Genetic algorithms genotypes

Fig. 3 An illustration of the genetic algorithm approach, where the states of the ligand (translation, orientation, and conformation relative to the protein) are interpreted as the ligand genotype and the atomic coordinates represent the phenotype. A plot of the change in the fitness function (f(x)) as the ligand population is allowed to mutate, crossover, and migrate. The genetic evolution of the ligand effectively samples conformational space where the best conformer is identified by a minimum in the fitness function (Reprinted with permission from [179], copyright 1998 by John Wiley and Sons)...

Genetic algorithms are typically used on function optimization problems, where they are used to calculate which input yields the maximum (or minimum) value. However, they have also been put to use on a wide variety of problems. These range from game-playing, where the genotype encodes a series of moves, to compiler optimization, in which case each gene is an optimization to be applied to a piece of code. 

Evolutionary optimisation methods are stochastic methods. This means that the available information about the objective funetion is complemented by random influences. The term evolutionary refers to the fact that the way of incorporating random influences into the optimisation process has in those methods been inspired by biological evolution. The most frequently used and most highly elaborated representatives of evolutionary methods are genetic algorithms (GAs), explained below, in which the incorporated random influences attempt to mimic the evolution of a genotype. Basically, this method involves ... 

Fallin D, Schork NJ. Accuracy of haplotype frequency estimation for biallelic loci via the expectation-maximization algorithm for unphased diploid genotype data. Am J Hum Genet 2000 67 947-959. 

O Connell, J. R. and Weeks, D. E. (1995). The VITESSE algorithm for rapid exact multilocus linkage analysis via genotype set-recoding and fuzzy inheritance. Nat. Genet. 11(4), 402-408. 

Genotype The collective genetic makeup of an organism. Often in evolutionary algorithms, the genotype and the chromosome are identical, but this is not always the case. 

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