Genetic algorithm generations

Other methods which are applied to conformational analysis and to generating multiple conformations and which can be regarded as random or stochastic techniques, since they explore the conformational space in a non-deterministic fashion, arc genetic algorithms (GA) [137, 1381 simulation methods, such as molecular dynamics (MD) and Monte Carlo (MC) simulations 1139], as well as simulated annealing [140], All of those approaches and their application to generate ensembles of conformations arc discussed in Chapter II, Section 7.2 in the Handbook. 

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

HTS data as well as virtual screening can guide and direct the design of combinatorial libraries. A genetic algorithm (GA) can be applied to the generation of combinatorial libraries [18. The number of compounds accessible by combinatorial synthesis often exceeds the number of compounds which can be syiithcsii ed... 

P Willett, J Bradshaw and D V S Green 1999. Selecting Combinatorial Libraries to Optimize rsity and Physical Properties. Journal of Chemical Information and Computer Science 39 169-177. 1 and A W R Payne 1995. A Genetic Algorithm for the Automated Generation of Molecules in Constraints. Journal of Computer-Aided Molecular Design 9 181-202. 

Fig. 5.9. Wavelength selection by Genetic Algorithm a 15 optimum wavelength selected from 86 of the full spectrum b relative fitness (rf/104) of the rim in dependence of the number of generations, above fitness of the best solution, middle mean, below fitness of the worst solution (Fischbacher et al. [1994/96 1995])... 

The size of initial population used in the genetic algorithm was 5 sequences. The size of crossover population was 2 sequences and the mutated population 2 sequences per generation. Consequently the total number of new sequences per generation was 4. The population size after selection was kept in 5. 

The genetic algorithm reached the solution usually in ten generations in this problem of more than 1100 different solutions. A random optimization would require tens of generations. The best solution found is the first configuration (23 12 14 11) in Figure 13. 

The genetic optimization was started with an initial population size of five, which was generated randomly. The algorithm included crossover of two sequences, which were selected randomly. Also random mutations were done on two sequences. The number of mutations per sequence varied from four in the beginning to one in the end per sequence. The steps of the genetic algorithm are ... 

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