Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Elements of information theory
Elements of information theory
A tractable Walsh analysis of SAT and its implications for genetic algorithms
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
Signal-to-noise, Crosstalk, and Long Range Problem Difficulty in Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Epistasis in Genetic Algorithms: An Experimental Design Perspective
Proceedings of the 6th International Conference on Genetic Algorithms
Voronoi Quantizied Crossover For Traveling Salesman Problem
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Genetic algorithms as function optimizers
Genetic algorithms as function optimizers
Efficient Linkage Discovery by Limited Probing
Evolutionary Computation
Linkage identification by non-monotonicity detection for overlapping functions
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
A survey on chromosomal structures and operators for exploiting topological linkages of genes
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
A meta-learning prediction model of algorithm performance for continuous optimization problems
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
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In optimization problems, the contribution of a variable to fitness often depends on the states of other variables. This phenomenon is referred to as epistasis or linkage. In this paper, we show that a new theory of epistasis can be established on the basis of Shannon's information theory. From this, we derive a new epistasis measure called entropic epistasis and some theoretical results. We also provide experimental results verifying the measure and showing how it can be used for designing efficient evolutionary algorithms.