Fuzzy Logic vs . Niched Pareto Multiobjective Genetic Algorithm Optimization : Part II : A Simplified Born - Mayer Problem
نویسنده
چکیده
A new multiobjective selection procedure for Genetic Algorithms based on the paradigms of fuzzy logic is discussed and compared to the niched Pareto selection procedure. In the example presented here the fuzzy logic procedure optimized the parameters of a series of functions in a more efficient manner than the niched Pareto approach. The main advantage that the fuzzy logic approach has over the niched Pareto approach is that its efficiency is completely independent of the number of objectives being optimized and its efficiency is highest with a comparison set size of 1 whereas the optimal comparison set size for the niched Pareto GA changes with the number of objectives. Furthermore, the fuzzy logic approach accounts for the existence of experimental errors in the values to which a function is being optimized. The functions explored in this work are the derivitives of a simplified Born-Mayer function used in molecular dynamic simulations.
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