Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Probabilistic modeling for continuous EDA with Boltzmann selection and Kullback-Leibeler divergence
Proceedings of the 8th annual conference on Genetic and evolutionary computation
The correlation-triggered adaptive variance scaling IDEA
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms (Studies in Fuzziness and Soft Computing)
Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence)
SDR: a better trigger for adaptive variance scaling in normal EDAs
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Convergence phases, variance trajectories, and runtime analysis of continuous EDAs
Proceedings of the 9th annual conference on Genetic and evolutionary computation
The equation for response to selection and its use for prediction
Evolutionary Computation
iBOA: the incremental bayesian optimization algorithm
Proceedings of the 10th annual conference on Genetic and evolutionary computation
On the convergence of a class of estimation of distribution algorithms
IEEE Transactions on Evolutionary Computation
A review on probabilistic graphical models in evolutionary computation
Journal of Heuristics
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In an Estimation of Distribution Algorithm (EDA) with an infinite sized population the selection distribution equals the search distribution. For a finite sized population these distributions are different. In practical EDAs the goal of the search distribution learning algorithm is to approximate the selection distribution. The source data is the selected set, which is derived from the population by applying a selection operator. The new approach described here eliminates the explicit use of the selection operator and the selected set. We rewrite for a finite population the selection distribution equations of four selection operators. The new equation is called the empirical selection distribution. Then we show how to build the search distribution that gives the best approximation to the empirical selection distribution. Our approach gives place to practical EDAs which can be easily and directly implemented from well established theoretical results. This paper also shows how common EDAs with discrete and real variables are adapted to take advantage of the empirical selection distribution. A comparison and discussion of performance is presented.