Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation
Machine Learning - Special issue on genetic algorithms
The evolution of evolvability in genetic programming
Advances in genetic programming
Foundations of genetic programming
Foundations of genetic programming
Fitness Distance Correlation And Problem Difficulty For Genetic Programming
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
A Study of Fitness Distance Correlation as a Difficulty Measure in Genetic Programming
Evolutionary Computation
Difficulty of unimodal and multimodal landscapes in genetic programming
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
A comparison of predictive measures of problem difficulty inevolutionary algorithms
IEEE Transactions on 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
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Evolving team behaviours in environments of varying difficulty
Artificial Intelligence Review
NK Landscapes Difficulty and Negative Slope Coefficient: How Sampling Influences the Results
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Dealings with problem hardness in genetic algorithms
WSEAS Transactions on Computers
The new negative slope coefficient measure
EC'09 Proceedings of the 10th WSEAS international conference on evolutionary computing
Fitness landscapes and problem hardness in genetic programming
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Quality measures to adapt the participation in MOS
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A comprehensive view of fitness landscapes with neutrality and fitness clouds
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
Negative slope coefficient and the difficulty of random 3-SAT instances
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Fitness landscapes and problem hardness in genetic programming
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Theoretical results in genetic programming: the next ten years?
Genetic Programming and Evolvable Machines
Open issues in genetic programming
Genetic Programming and Evolvable Machines
Learning hybridization strategies in evolutionary algorithms
Intelligent Data Analysis
Practical performance models of algorithms in evolutionary program induction and other domains
Artificial Intelligence
Fitness-probability cloud and a measure of problem hardness for evolutionary algorithms
EvoCOP'11 Proceedings of the 11th European conference on Evolutionary computation in combinatorial optimization
A study of the neutrality of Boolean function landscapes in genetic programming
Theoretical Computer Science
Fitness landscapes and graphs: multimodularity, ruggedness and neutrality
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
A survey of techniques for characterising fitness landscapes and some possible ways forward
Information Sciences: an International Journal
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Negative 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 trees.