Abstract
Recently the Generalized Genetic Algorithm has been proposed, based on genetic algorithms and Tsallis statistics. Here, we study molecular preferences of the simple molecules and polypeptides by coupling the Generalized Genetic Algorithm and both a classical force field and a semi-empirical quantum mechanics procedure. The main characteristics of this stochastic algorithm is the capability to find the greatest number of minima for a given energy in an N -dimensional hyper-surface supplying information about the visiting rates for each one of the conformational structures. The main advantage of this method over other global optimization procedures is that this approach provides additional information about visiting frequency of all local and global minima. These characteristics may provide the populations of each conformation without using the (quantum or classical) Monte Carlo procedure.
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