Sporadic model building for efficiency enhancement of the hierarchical BOA

Efficiency enhancement techniques--such as parallelization and hybridization--are among the most important ingredients of practical applications of genetic and evolutionary algorithms and that is why this research area represents an important niche of evolutionary computation. This paper describes and analyzes sporadic model building, which can be used to enhance the efficiency of the hierarchical Bayesian optimization algorithm (hBOA) and other estimation of distribution algorithms (EDAs) that use complex multivariate probabilistic models. With sporadic model building, the structure of the probabilistic model is updated once in every few iterations (generations), whereas in the remaining iterations, only model parameters (conditional and marginal probabilities) are updated. Since the time complexity of updating model parameters is much lower than the time complexity of learning the model structure, sporadic model building decreases the overall time complexity of model building. The p...

• Type: Journal
• Year: 2008
• Where: GPEM
• Authors: Martin Pelikan, Kumara Sastry, David E. Goldberg
• Keywords: GPEM 2008, Model, Sporadic Model, Time Complexity