Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Fitness-proportional negative slope coefficient as a hardness measure for genetic algorithms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A fine-grained view of GP locality with binary decision diagrams as ant phenotypes
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Defining locality as a problem difficulty measure in genetic programming
Genetic Programming and Evolvable Machines
A study of the neutrality of Boolean function landscapes in genetic programming
Theoretical Computer Science
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Fitness-Proportional Negative Slope Coefficient is a fitness landscapes measure that has recently been introduced as a potential indicator of problem hardness for optimisation. It is inspired to an older measure, the Negative Slope Coefficient, and it has been theoretically modelled. Preliminary experiments have suggested that it may be a good predictor of problem hardness. However, this measure has not undergone any convincing and comprehensive empirical testing. Our objective is to fill this gap. So, we perform empirical tests using a large set of invertible functions of unitation. We find that while this measure may correctly predict the degree of evolvability of a landscape, this does not necessarily correlate with the difficulty of problems. Some landscapes may show, for example, limited evolvability and yet be easy to solve because either solutions are already present in the initial population or the computational resources provided exceed evolvability obstacles. Or it may be impossible to solve them irrespective of their evolvability simply because they are far too vast for the computational resources provided. These situations are hardly captured by the Fitness-Proportional Negative Slope Coefficient.