Exploiting the deep structure of constraint problems
Artificial Intelligence
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
On the classification of NP-complete problems in terms of their correlation coefficient
Proceedings of the 5th Twente workshop on on Graphs and combinatorial optimization
Phase Transitions in Combinatorial Optimization Problems - Basics, Algorithms and Statistical Mechanics
Backbone fragility and the local search cost peak
Journal of Artificial Intelligence Research
Hierarchical hardness models for SAT
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Clustering at the phase transition
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Fitness landscape analysis and memetic algorithms for the quadratic assignment problem
IEEE Transactions on Evolutionary Computation
NK landscapes, problem difficulty, and hybrid evolutionary algorithms
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Elementary landscapes of frequency assignment problems
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Elementary landscape decomposition of the quadratic assignment problem
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Elementary bit string mutation landscapes
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Advanced neighborhoods and problem difficulty measures
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Exact computation of the expectation curves of the bit-flip mutation using landscapes theory
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Elementary landscape decomposition of the test suite minimization problem
SSBSE'11 Proceedings of the Third international conference on Search based software engineering
A methodology to find the elementary landscape decomposition of combinatorial optimization problems
Evolutionary Computation
EvoCOP'12 Proceedings of the 12th European conference on Evolutionary Computation in Combinatorial Optimization
Exact computation of the expectation curves for uniform crossover
Proceedings of the 14th annual conference on Genetic and evolutionary computation
The lay of the land: a brief survey of problem understanding
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Problem understanding through landscape theory
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Adaptation of a multiagent evolutionary algorithm to NK landscapes
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Elementary landscape decomposition of the 0-1 unconstrained quadratic optimization
Journal of Heuristics
Hi-index | 0.00 |
The autocorrelation function and related correlation length are statistical quantities that capture the ruggedness of the fitness landscape: a measure that is directly related to the hardness of a problem for certain heuristic search algorithms. Typically, these quantities are estimated empirically by sampling along a random walk. In this paper, we show that a polynomial-time Walsh decomposition of the k-satisfiability evaluation function allows us to compute the exact autocorrelation function and correlation length for any given k-satisfiability instance. We also use the decomposition to compute a theoretical expectation for the autocorrelation function and correlation length over the ensemble of instances generated uniformly at random. We find that this expectation is invariant to the constrainedness of the problem as measured by the ratio of clauses to variables. However, we show that filtered problems, which are typically used in local search studies, have a bias that causes a significant deviation from the expected correlation structure of unfiltered, uniformly generated problems.