Chromosome genetic algorithm

The evolutionary process of a genetic algorithm is accomplished by genetic operators which translate the evolutionary concepts of selection, recombination or crossover, and mutation into data processing to solve an optimization problem dynamically. Possible solutions to the problem are coded as so-called artificial chromosomes, which are changed and adapted throughout the optimization process until an optimrun solution is obtained. 

A key feature ofa genetic algorithm is that only the best chromosomes are to pass their features to the next generation during evolution. 

The chromosome in a genetic algorithm codes for the torsion angles of the rotatable bonds. 

Genetic algorithms can also be used to perform molecular docking [Judson et d. 1994 Jont et d. 1995b Oshiro et d. 1995]. Each chromosome codes not only for the internal conform tion of the ligand as described in Section 9.9.1 but also for the orientation of the ligand withi the receptor site. Both the orientation and the internal conformation will thus vary as th populations evolve. The score of each docked structure within the site acts as the fitnes function used to select the individuals for the next iteration. 

Table 11.3 One pass (read left to right) through the step.s of a basic genetic algorithm scheme to maximize the fitness function f x) = using a population of six 6-bit chromosomes. The crossover notation aina2) means that chromosomes Ca, and Ca2 exchange bits beyond the bit. The underlined bits in the Mutation Operation column are the only ones that have undergone random mutation. See text for other details.

If the goal of a genetic algorithm application was to find the lowest energy arrangement of the atoms in bromochloromethane (Figure 5.5), the chromosome that defines a possible solution to the problem could be formed as an ordered list of the Cartesian coordinates of each atom ... 

The vector that the genetic algorithm manipulates is known conventionally as a chromosome or a string we shall use the latter terminology in this chapter. The individual units from which each string is constructed, a single x-, y-, or z-coordinate for an atom in this example, are referred to as genes. 

These results were obtained by coupling a genetic algorithm for descriptor and calculation parameter (PC, bins) selection to PCA-based partitioning. In these calculations, descriptors were chosen from a pool of approx 150 different ones, and both the number of PCs and bins were allowed to vary from 1 to 15. An initial population of 300 chromosomes was randomly generated with initial bit occupancy of approx 15%. Rates for mutation and crossover operations were set to 5% and 25%, respectively. After PCA-based partitioning, scores were calculated for the following fitness function ... 

Algorithm GASP uses a genetic algorithm that iteratively optimizes a population of chromosomes according to the fitness function described here below [16]. Each chromosome encodes angles of rotations about flexible bonds and mappings between features. 

Variable selection is performed by using Genetic Algorithms (GA), based on the evolution of a population of models. In genetic algorithm terminology, the binary vector I is called a chromosome, which is a p-dimensional vector where each position (a gene) corresponds to a variable (1 if included in the model, 0 otherwise). Each chromosome represents a model with a subset of variables. 

Another application of GAs was published by Aires de Sousa et al. they used genetic algorithms to select the appropriate descriptors for representing structure-chemical shift correlations in the computer [69]. Each chromosome was represented by a subset of 486 potentially useful descriptors for predicting H-NMR chemical shifts. The task of a fitness function was performed by a CPG neural network that used the subset of descriptors encoded in the chromosome for predicting chemical shifts. Each proton of a compound is presented to the neural network as a set of descriptors obtaining a chemical shift as output. The fitness function was the RMS error for the chemical shifts obtained from the neural network and was verified with a cross-validation data set. 

Chromosome in a genetic algorithm is a representation for data and solutions consisting of real numbers or bit values that represent the presence or absence of features. 

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