Real and complex analysis, 3rd ed.
Real and complex analysis, 3rd ed.
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Genetic Algorithm and Graph Partitioning
IEEE Transactions on Computers
Spin-flip symmetry and synchronization
Evolutionary Computation
Proceedings of the 5th 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
Modeling Building-Block Interdependency
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
A New Genetic Local Search Algorithm for Graph Coloring
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Isomorphism, Normalization, And A Genetic Algorithm For Sorting Network Optimization
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
From Twomax To The Ising Model: Easy And Hard Symmetrical Problems
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
A Critical and Empirical Study of Epistasis Measures for Predicting GA Performances: A Summary
AE '97 Selected Papers from the Third European Conference on Artificial Evolution
Redundant representations in evolutionary computation
Evolutionary Computation
Fitness Landscapes, Memetic Algorithms, and Greedy Operators for Graph Bipartitioning
Evolutionary Computation
Predicting epistasis from mathematical models
Evolutionary Computation
Fitness landscape analysis and memetic algorithms for the quadratic assignment problem
IEEE Transactions on Evolutionary Computation
The analysis of a recombinative hill-climber on H-IFF
IEEE Transactions on Evolutionary Computation
Symmetry at the genotypic level and the simple inversion operator
EPIA'07 Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence
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The properties of symmetric fitness functions are investigated. We show that a well-known encoding scheme inducing symmetric functions has the non-synonymous property and the search spaces obtained from symmetric functions have the zero-correlation structures. The Walsh analysis reveals the properties of symmetric functions related to additive separability, problem difficulty measures and so on. Our results support the claim of other researchers that the search spaces with symmetry induce relatively difficult problems. The results also present some limitations of existing problem difficulty measures for symmetric fitness functions.