Paper Info
Title
Analyzing the probability of the optimum in EDAs based on Bayesian networks
Abstract
In this paper we quantitatively analyze the probability distributions generated by an EDA during the search. In particular, we record the probabilities to the optimal solution, the solution with the highest probability and that of the best individual of the population, when the EDA is solving a trap function. By using different structures in the probabilistic models we can analyze the influence of the structural model accuracy on the aforementioned probability values. In addition, the objective function values of these solutions are contrasted with their probability values in order to study the connection between the function and the probabilistic model. The results provide new information about the behavior of the EDAs and they open a discussion regarding which are the minimum (in)dependences necessary to reach the optimum.
Year
DOI
Venue
2009
10.1109/CEC.2009.4983140
IEEE Congress on Evolutionary Computation
Keywords
Field
DocType
objective function value,probabilistic model,optimal solution,bayesian network,aforementioned probability value,best individual,probability value,highest probability,structural model accuracy,trap function,probability distribution,computational modeling,genetic algorithms,bayesian methods,evolutionary computation,probabilistic logic,estimation,data mining,artificial intelligence,bayesian networks,statistical distributions,estimation of distribution algorithm,objective function,probability density function
Convolution of probability distributions,EDAS,Mathematical optimization,Computer science,Empirical probability,Bayesian network,Probability distribution,Artificial intelligence,Probabilistic logic,Probability density function,Machine learning,Bayesian probability
Conference
Citations 
PageRank 
References 
3
0.37
8
Authors
4
Name
Order
Citations
PageRank
Carlos Echegoyen1532.94
Alexander Mendiburu235533.61
Roberto Santana335719.04
José A. Lozano42148167.25