Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Representations for Genetic and Evolutionary Algorithms
Representations for Genetic and Evolutionary Algorithms
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Information landscapes and the analysis of search algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Classes of problems in the black box scenario
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Real options approach to evaluating genetic algorithms
Applied Soft Computing
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
Quantifying ruggedness of continuous landscapes using entropy
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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
Evolutionary algorithm characterization in real parameter optimization problems
Applied Soft Computing
A survey of techniques for characterising fitness landscapes and some possible ways forward
Information Sciences: an International Journal
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In [20] we introduced a new concept of a landscape: the information landscape. We showed that for problems of very small size (e.g. a 3-bit problem), it can be used to generally and accurately predict the performance of a GA. Based on this framework, in this paper we develop a method to predict GA hardness on realistic landscapes. We give empirical results which support our approach.