The ineffectiveness of recombination in a genetic algorithm for the structure elucidation of a heptapeptide in torsion angle space. A comparison to simulated annealing

Abstract Genetic algorithms comprise a family of stochastic optimization strategies, which are often applied to solve complex optimization problems. The combination of population based search and a recombination operator distinguishes the genetic algorithm from other global optimization techniques that often only comprise a (sophisticated) mutation-selection scheme. Investigations were conducted that suggest, however, that recombination is not always effective, i.e., crossover was unable to recombine the so-called building blocks that should produce improved trial solutions. In this research the contribution of the crossover operation to the performance of the genetic algorithm was examined for a structure elucidation problem of a heptapeptide. In addition, the performance of the genetic algorithm was compared to the alternative simulated annealing strategy. It was shown that the current design of the genetic algorithm did not promote the recombination of building blocks, and was therefore easily outperformed by simulated annealing. The strategy presented to reveal the effectiveness of recombination is straightforward, and can easily applied to other genetic algorithm applications.

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