The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
Epistasis in Genetic Algorithms: An Experimental Design Perspective
Proceedings of the 6th International Conference on Genetic Algorithms
Estimating the Heritability by Decomposing the Genetic Variance
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Limitations of existing mutation rate heuristics and how a rank GA overcomes them
IEEE Transactions on Evolutionary Computation
New entropy-based measures of gene significance and epistasis
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
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In literature, GA hardness has been studied as the product of only one source or several quasi separable sources; however none of such approaches has been successful. In addition, several hardness models have been conceived in order to analyze, quantify and/or predict difficulty, despite most of them are not able to describe hardness in a suitable way. How hardness is affected by the amount and type of information inherent to the problem seems to be a promising perspective. This work is then a preliminary empirical study that proposes hardness taxonomy to classify problems using a combination of two broad-spectrum sources: amount and type of information.