Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Randomized algorithms
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
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Concrete Math
Genetic Algorithm and Graph Partitioning
IEEE Transactions on Computers
Probabilistic Incremental Program Evolution: Stochastic Search Through Program Space
ECML '97 Proceedings of the 9th European Conference on Machine Learning
RapidAccurate Optimization of Difficult Problems Using Fast Messy Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Gene Expression and Fast Construction of Distributed Evolutionary Representation
Evolutionary Computation
Efficient Linkage Discovery by Limited Probing
Evolutionary Computation
Space Complexity of Estimation of Distribution Algorithms
Evolutionary Computation
gLINC: identifying composability using group perturbation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Multi-attractor gene reordering for graph bisection
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Upper and Lower Bounds for Randomized Search Heuristics in Black-Box Optimization
Theory of Computing Systems
Predicting epistasis from mathematical models
Evolutionary Computation
Fda -a scalable evolutionary algorithm for the optimization of additively decomposed functions
Evolutionary Computation
Probabilistic computations: Toward a unified measure of complexity
SFCS '77 Proceedings of the 18th Annual Symposium on Foundations of Computer Science
Hierarchical BOA solves ising spin glasses and MAXSAT
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Efficient linkage discovery by limited probing
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
The computational complexity of N-K fitness functions
IEEE Transactions on Evolutionary Computation
Properties of Symmetric Fitness Functions
IEEE Transactions on Evolutionary Computation
GRCA: a hybrid genetic algorithm for circuit ratio-cut partitioning
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Optimal query complexity bounds for finding graphs
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Optimal query complexity bounds for finding graphs
Artificial Intelligence
A surrogate-assisted linkage inference approach in genetic algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Almost tight upper bound for finding Fourier coefficients of bounded pseudo-Boolean functions
Journal of Computer and System Sciences
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For a real-valued function f defined on {0, 1}n, the linkage graph of f is a hypergraph that represents the interactions among the input variables with respect to f. In this paper, lower and upper bounds for the number of function evaluations required to discover, the linkage graph are rigorously analyzed in the black box scenario. First, a lower bound for discovering linkage graph is presented, To the best of our knowledge, this is the first result on the lower bound for linkage discovery. The investigation on the lower bound is based on Yao's minimax principle. For the upper bounds, a simple randomized algorithm for linkage discovery is analyzed. Based on the Kruskal-Katona theorem, we present an upper bound for discovering the linkage graph. As a corollary, we rigorously prove that O(n2 log n) function evaluations are enough for bounded functions when the number of hyperedges is O(n), which was suggested but not proven in previous works. To see the typical behavior of the algorithm for linkage discovery, three random models of fitness functions are considered. Using probabilistic methods, we prove that the number of function evaluations on the random models is generally smaller than the bound for the arbitrary case. Finally, from the relation between the linkage graph and the Walsh coefficients, it is shown that, for bounded functions, the proposed bounds are eventually the bounds for finding the Walsh coefficients.