Randomized algorithms
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
The Importance of Selection Mechanisms in Distribution Estimation Algorithms
Selected Papers from the 5th European Conference on Artificial Evolution
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Bayesian optimization algorithm: from single level to hierarchy
Bayesian optimization algorithm: from single level to hierarchy
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
The equation for response to selection and its use for prediction
Evolutionary Computation
Does overfitting affect performance in estimation of distribution algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Addressing sampling errors and diversity loss in UMDA
Proceedings of the 9th annual conference on Genetic and evolutionary computation
UMDAs for dynamic optimization problems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
On the Parallel Speed-Up of Estimation of Multivariate Normal Algorithm and Evolution Strategies
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Why one must use reweighting in estimation of distribution algorithms
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Quantum-inspired evolutionary algorithm: a multimodel EDA
IEEE Transactions on Evolutionary Computation - Special issue on evolutionary algorithms based on probabilistic models
Information Sciences: an International Journal
Diversity loss in general estimation of distribution algorithms
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Global optimal selection of web composite services based on UMDA
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
On the taxonomy of optimization problems under estimation of distribution algorithms
Evolutionary Computation
| Hi-index | 0.00 |
This paper considers a phenomenon in Estimation of Distribution Algorithms (EDA) analogous to drift in population genetic dynamics. Finite population sampling in selection results in fluctuations which get reinforced when the probability model is updated. As a consequence, any probability model which can generate only a single set of values with probability 1 can be an attractive fixed point of the algorithm. To avoid this, parameters of the algorithm must scale with the system size in strongly problemdependent ways, or the algorithm must be modified. This phenomenon is shown to hold for general EDAs as a consequence of the lack of ergodicity and irreducibility of the Markov chain on the state of probability models. It is illustrated in the case of UMDA, in which it is shown that the global optimum is only found if the population size is sufficiently large. For the needle-in-a haystack problem, the population size must scale as the square-root of the size of the search space. For the one-max problem, the population size must scale as the square-root of the problem size.