Negative Slope Coefficient: A Measure to Characterize Genetic Programming Fitness Landscapes | Zendy

Leonardo Vanneschi | Zendy; Marco Tomassini | Zendy; Philippe Collard | Zendy; Sebástien Vérel | Zendy

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Negative Slope Coefficient: A Measure to Characterize Genetic Programming Fitness Landscapes

Author(s) -

Leonardo Vanneschi,

Marco Tomassini,

Philippe Collard,

Sebástien Vérel

Publication year - 2006

Publication title -

lecture notes in computer science

Language(s) - English

Resource type - Book series

SCImago Journal Rank - 0.249

H-Index - 400

eISSN - 1611-3349

pISSN - 0302-9743

ISBN - 3-540-33143-3

DOI - 10.1007/11729976_16

Subject(s) - genetic programming , measure (data warehouse) , computer science , multiplexer , genetic algorithm , ant colony optimization algorithms , mathematical optimization , artificial intelligence , data mining , mathematics , machine learning , multiplexing , telecommunications

International audienceNegative slope coefficient has been recently introduced and empirically proven a suitable hardness indicator for some well known genetic programming benchmarks, such as the even parity problem, the binomial-3 and the artificial ant on the Santa Fe trail. Nevertheless, the original definition of this measure contains several limitations. This paper points out some of those limitations, presents a new and more relevant definition of the negative slope coefficient and empirically shows the suitability of this new definition as a hardness measure for some genetic programming benchmarks, including the multiplexer, the intertwined spirals problem and the royal tree

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