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We introduce a novel method called subdividing labeling genetic algorithm (SLGA) to solve optimization problems involving n – dimensional continuous nonlinear functions. SLGA is based on the mutation and crossover operators of genetic algorithms, which are applied on a subdivided search space where an integer label is defined on a polytope built on the n – dimensional space. The SLGA method approaches...
In most global optimization problems, finding a global optimum point in the whole multi-dimensional search space implies a high computational burden. We present a new approach called subdividing labeling genetic algorithm (SLGA) for continuous nonlinear optimization problems. SLGA applies mutation and crossover operators on a subdivided search space where an integer label is defined on a polytope...
In this study, a new multi-criteria classification technique for nominal and ordinal groups is developed by expanding the UTilites Additives DIScriminantes (UTADIS) method with a polynomial of degree T which is used as the utility function rather than using a piecewise linear function as an approximation of the utility function of each attribute. We called this method as PUTADIS. The objective is...
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