Enhancement of Multiobjective Hierarchical Bayesian Optimization Algorithm using Sporadic Model Building

This paper describes and analyzes the efficiency enhancement of Multiobjective hierarchical Bayesian Optimization Algorithm (mohBOA) by using Sporadic Model Building (SMB). Firstly, Multiobjective hierarchical Bayesian Optimization Algorithm is shortly described. Secondly, sporadic model building is presented. Using sporadic model building, the structure of a probabilistic model is updated once every few iterations, whereas in the remaining iterations only model parameters (conditional and marginal probabilities) are updated. Since the time of learning the structure of a model is much longer than the time of updating model parameters, sporadic model building decreases the total time complexity of model building. The results of experiments show that the theoretical predictions about using sporadic model building to the enhancement of mohBOA are true. Finally, short discussion about the results of experiments is added

Enhancement of Multiobjective Hierarchical Bayesian Optimization Algorithm using Sporadic Model Building

This paper describes and analyzes the efficiency enhancement of Multiobjective hierarchical Bayesian Optimization Algorithm (mohBOA) by using Sporadic Model Building (SMB). Firstly, Multiobjective hierarchical Bayesian Optimization Algorithm is shortly described. Secondly, sporadic model building is presented. Using sporadic model building, the structure of a probabilistic model is updated once every few iterations, whereas in the remaining iterations only model parameters (conditional and marginal probabilities) are updated. Since the time of learning the structure of a model is much longer than the time of updating model parameters, sporadic model building decreases the total time complexity of model building. The results of experiments show that the theoretical predictions about using sporadic model building to the enhancement of mohBOA are true. Finally, short discussion about the results of experiments is added

Combinatorial optimization and metaheuristics

Today, combinatorial optimization is one of the youngest and most active areas of discrete mathematics. It is a branch of optimization in applied mathematics and computer science, related to operational research, algorithm theory and computational complexity theory. It sits at the intersection of several fields, including artificial intelligence, mathematics and software engineering. Its increasing interest arises for the fact that a large number of scientific and industrial problems can be formulated as abstract combinatorial optimization problems, through graphs and/or (integer) linear programs. Some of these problems have polynomial-time (“efficient”) algorithms, while most of them are NP-hard, i.e. it is not proved that they can be solved in polynomial-time. Mainly, it means that it is not possible to guarantee that an exact solution to the problem can be found and one has to settle for an approximate solution with known performance guarantees. Indeed, the goal of approximate methods is to find “quickly” (reasonable run-times), with “high” probability, provable “good” solutions (low error from the real optimal solution). In the last 20 years, a new kind of algorithm commonly called metaheuristics have emerged in this class, which basically try to combine heuristics in high level frameworks aimed at efficiently and effectively exploring the search space. This report briefly outlines the components, concepts, advantages and disadvantages of different metaheuristic approaches from a conceptual point of view, in order to analyze their similarities and differences. The two very significant forces of intensification and diversification, that mainly determine the behavior of a metaheuristic, will be pointed out. The report concludes by exploring the importance of hybridization and integration methods

MATEDA: A suite of EDA programs in Matlab

This paper describes MATEDA-2.0, a suite of programs in Matlab for estimation of distribution algorithms. The package allows the optimization of single and multi-objective problems with estimation of distribution algorithms (EDAs) based on undirected graphical models and Bayesian networks. The implementation is conceived for allowing the incorporation by the user of different combinations of selection, learning, sampling, and local search procedures. Other included methods allow the analysis of the structures learned by the probabilistic models, the visualization of particular features of these structures and the use of the probabilistic models as fitness modeling tools