Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Effects of Population Size and Mutation Rate on Results of Genetic Algorithm
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 01
Pyevolve: a Python open-source framework for genetic algorithms
ACM SIGEVOlution
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The determination of the lowest energy conformer for long-chain molecules by exhaustive search methods quickly becomes infeasible as the length increases. Typically, resources required are proportional to the number of possible conformers (shapes), O(3^n) where n is the length. A genetic algorithm (GA) that calculates energies in a feasible time is described, using an open-source off-the shelf tool, PyEvolve. By comparing the results using this method with the results from exhaustive search techniques on carnosine, a dipeptide whose energy calculation is currently near the limits of feasibility using exhaustive search methods (n = 8), we obtained quantitative measurements of the performance of this GA. Optimization of a subset of the GAs parameters in a non-adaptive GA was accomplished by encoding the parameters into a genome, and using a meta-GA to tune the algorithm.. Our results suggest that PyEvolve's simple GAs with our experimentally-determined parameter values are a computationally feasible method of determining long-chain molecular energies computationally infeasible using other methods.