engleski

The New Negative Slope Coefficient Measure

When is a problem easy or difficult for a genetic algorithm? This work focuses on unitation functions as tests for the efficiency of a genetic algorithm in reaching an optimal solution. We research the effectiveness of the Negative Slope Coefficient Measure (NSC measure) in finding difficult problems and present flaws of such a measure. In summary, we present a new measure for defining the hardness of a problem, the new NSC, based on the Fitness Landscape ; experimentally we demonstrate the efficacy of the method and compare it with the performance measure achieved by real runs. Finally we propose new steps for development of the method.

Genetic Algorithm ; Unitation ; Fitness Landscape ; Negative Slope Coefficient ; Hardness

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