Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Morphing: combining structure and randomness
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Bucket elimination: a unifying framework for reasoning
Artificial Intelligence
Properties of fitness functions and search landscapes
Theoretical aspects of evolutionary computing
Statistical mechanics methods and phase transitions in optimizationproblems
Theoretical Computer Science - Phase transitions in combinatorial problems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Treewidth and Minimum Fill-in: Grouping the Minimal Separators
SIAM Journal on Computing
Schemata, Distributions and Graphical Models in Evolutionary Optimization
Journal of Heuristics
RapidAccurate Optimization of Difficult Problems Using Fast Messy Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Treewidth: Algorithmoc Techniques and Results
MFCS '97 Proceedings of the 22nd International Symposium on Mathematical Foundations of Computer Science
Combinatonal Optimization by Learning and Simulation of Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
On the futility of blind search: An algorithmic view of “no free lunch”
Evolutionary Computation
An analysis of phase transition in NK landscapes
Journal of Artificial Intelligence Research
On the treewidth of NK landscapes
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Efficient linkage discovery by limited probing
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
A sufficiently fast algorithm for finding close to optimal junction trees
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Phase transition of tractability in constraint satisfaction and bayesian network inference
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Neural Networks
Approximate factorizations of distributions and the minimum relative entropy principle
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Lower and upper bounds for linkage discovery
IEEE Transactions on Evolutionary Computation
Performance of evolutionary algorithms on random decomposable problems
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
On the taxonomy of optimization problems under estimation of distribution algorithms
Evolutionary Computation
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In this paper, we investigate the space complexity of the Estimation of Distribution Algorithms (EDAs), a class of sampling-based variants of the genetic algorithm. By analyzing the nature of EDAs, we identify criteria that characterize the space complexity of two typical implementation schemes of EDAs, the factorized distribution algorithm and Bayesian network-based algorithms. Using random additive functions as the prototype, we prove that the space complexity of the factorized distribution algorithm and Bayesian network-based algorithms is exponential in the problem size even if the optimization problem has a very sparse interaction structure.