Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Evolutionary computation and Wright's equation
Theoretical Computer Science - Natural computing
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
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PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Combinatonal Optimization by Learning and Simulation of Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
The equation for response to selection and its use for prediction
Evolutionary Computation
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This work presents an analysis of the convergence behaviour of the Univariate Marginal Distribution Algorithm (UMDA) when it is used to maximize a number of pseudo-boolean functions. The analysis is based on modeling the algorithm using a reducible Markov chain, whose absorbing states correspond to the individuals of the search space. The absorption probability to the optimum and the expected time of convergence to the set of absorbing states are calculated for each function. This information is used to provide some insights into how the absorption probability to the optimum and the expected absorption times evolve when the size of population increases. The results show the different behaviours of the algorithm in the analyzed functions.