GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A step forward in studying the compact genetic algorithm
Evolutionary Computation
Detecting the epistatic structure of generalized embedded landscape
Genetic Programming and Evolvable Machines
Wrapper discretization by means of estimation of distribution algorithms
Intelligent Data Analysis
Convergence analysis of UMDAC with finite populations: a case study on flat landscapes
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Analysis of evolutionary algorithms on the one-dimensional spin glass with power-law interactions
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Structure learning and optimisation in a Markov-network based estimation of distribution algorithm
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
On multivariate genetic systems
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Analyzing probabilistic models in hierarchical BOA
IEEE Transactions on Evolutionary Computation - Special issue on evolutionary algorithms based on probabilistic models
Quantum-inspired evolutionary algorithm: a multimodel EDA
IEEE Transactions on Evolutionary Computation - Special issue on evolutionary algorithms based on probabilistic models
Effective linkage learning using low-order statistics and clustering
IEEE Transactions on Evolutionary Computation - Special issue on evolutionary algorithms based on probabilistic models
Analysis of computational time of simple estimation of distribution algorithms
IEEE Transactions on Evolutionary Computation
Information Sciences: an International Journal
On the optimal convergence probability of univariate estimation of distribution algorithms
Evolutionary Computation
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
A review on probabilistic graphical models in evolutionary computation
Journal of Heuristics
Variable transformations in estimation of distribution algorithms
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
On the taxonomy of optimization problems under estimation of distribution algorithms
Evolutionary Computation
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Aims to study the advantages of using higher order statistics in estimation distribution of algorithms (EDAs). We study two EDAs with two-tournament selection for discrete optimization problems. One is the univariate marginal distribution algorithm (UMDA) using only first-order statistics and the other is the factorized distribution algorithm (FDA) using higher order statistics. We introduce the heuristic functions and the limit models of these two algorithms and analyze stability of these limit models. It is shown that the limit model of UMDA can be trapped at any local optimal solution for some initial probability models. However, degenerate probability density functions (pdfs) at some local optimal solutions are unstable in the limit model of FDA. In particular, the degenerate pdf at the global optimal solution is the unique asymptotically stable point in the limit model of FDA for the optimization of an additively decomposable function. Our results suggest that using higher order statistics could improve the chance of finding the global optimal solution.