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 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)
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)
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)
Selection of run-time parameters is a critical step in the application of genetic algorithms. Numerous investigations have discussed parameter set selection, both theoretically and empirically. Theoretical work has focused on the choice of population size 7, 8, 9, 13, 16], while empirical studies cover a wide range of GA parameters 3, 4, 10, 15]. Theory(More)
The fast messy genetic algorithm (fmGA) belongs to a class of algorithms inspired by the principles of evolution, known appropriately as "evolutionary algorithms" (EAs). These techniques operate by applying biologically-inspired operators, such as recom-bination, mutation, and selection, to a population of individuals. EAs are frequently applied as optimum(More)
| T o e v aluate the performance of a real-valued genetic algorithm (GA) exploiting domain knowledge, we systematically evaluate the eeect of exogenous parameters using analysis of variance. The GA platform used for this study is Genocop-III, a real-valued, co-evolutionary algorithm implementation for numerical optimization. We u s e the protein structure(More)
Energy minimization eeorts to predict polypeptide structures assume their native conformation corresponds to the global minimum free energy state. Given this assumption, the problem becomes that of developing eecient global optimization techniques applicable to polypeptide energy models. This general structure prediction objective is also known as the(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)