George H. Gates

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Efforts to predict polypeptide structures nearly always assume that the native conformation corresponds to the global minimum free energy state of the system. Given this assumption, a necessary step in solving the problem is the development of efficient global energy minimization techniques. We describe a hybrid genetic algorithm which incorporates(More)
A b s t r a c t Energy minimization efforts to predict polypeptide structures assuule their native conformation corresponds to the global minimum free energy state. Given this assumption, the problem becomes that of developing efficient global optinfization techniques applicable to polypeptide energy models. This general structure prediction objective is(More)
| A hybrid genetic algorithm for polypeptide structure prediction is proposed which incorporates ee-cient gradient-based minimization directly in the tness evaluation. Fitness is based on a polypeptide speciic potential energy model. The algorithm includes a replacement frequency parameter which speciies the probability with which an individual is replaced(More)
Accurate and reliable protein structure prediction (PSP) eludes researchers primarily because the search for the minimum energy conformer is computationally intractable. This research discusses the application of several distinct genetic algorithms (GAs) as optimum seeking techniques for PSP problems. The eeectiveness and eeciency of each algorithm is(More)
This paper provides insight into combining stochastic and deterministic search methods using evolutionary algorithms (EAs) such as evolutionary programming, evolutionary strategies, and genetic algorithms integrated with depth-rst search with backtracking, branch and bound, and best-rst search algorithms such as A. An important view of such an integration(More)
The fast messy genetic algorithm (fmGA) belongs to a class of algorithms inspired by the principles of evolution, known appropriately as "evolutionary algo-rithms" (EAs). These techniques operate by applying biologically-inspired operators, such as recombination, mutation, and selection, to a population of individuals. EAs are frequently applied as optimum(More)
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