Randomized algorithms
Drift analysis and average time complexity of evolutionary algorithms
Artificial Intelligence
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
A study of drift analysis for estimating computation time of evolutionary algorithms
Natural Computing: an international journal
A rigorous analysis of the compact genetic algorithm for linear functions
Natural Computing: an international journal
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Rigorous time complexity analysis of univariate marginal distribution algorithm with margins
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Analysis of computational time of simple estimation of distribution algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Evolutionary Computation
Rigorous time complexity analysis of univariate marginal distribution algorithm with margins
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Simple max-min ant systems and the optimization of linear pseudo-boolean functions
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Unpacking and understanding evolutionary algorithms
WCCI'12 Proceedings of the 2012 World Congress conference on Advances in Computational Intelligence
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Despite the wide-spread popularity of estimation of distribution algorithms (EDAs), there has been no theoretical proof that there exist optimisation problems where EDAs perform significantly better than traditional evolutionary algorithms. Here, it is proved rigorously that on a problem called SUBSTRING, a simple EDA called univariate marginal distribution algorithm (UMDA) is efficient, whereas the (1+1) EA is highly inefficient. Such studies are essential in gaining insight into fundamental research issues, i.e., what problem characteristics make an EDA or EA efficient, under what conditions an EDA is expected to outperform an EA, and what key factors are in an EDA that make it efficient or inefficient.