What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation
Machine Learning - Special issue on genetic algorithms
An introduction to genetic algorithms
An introduction to genetic algorithms
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Practical Genetic Algorithms with CD-ROM
Practical Genetic Algorithms with CD-ROM
Information landscapes and problem hardness
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A Study of Fitness Distance Correlation as a Difficulty Measure in Genetic Programming
Evolutionary Computation
Fitness-proportional negative slope coefficient as a hardness measure for genetic algorithms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Negative slope coefficient: a measure to characterize genetic programming fitness landscapes
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Dealings with problem hardness in genetic algorithms
WSEAS Transactions on Computers
Comparison of a crossover operator in binary-coded genetic algorithms
WSEAS Transactions on Computers
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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.