The process of learning Bayesian networks includes structure learning and parameters learning. During the process, learning the structure of Bayesian networks based on a large database is a NP hard problem. The paper presents a new hybrid algorithm by integrating the algorithms of MMPC (max-min parents and children), PSO (particle swarm optimization) and GA (genetic algorithm) effectively. In t...

Genetic Algorithm (GA) is effective and robust method for solving many optimization problems. However, it may take more runs (iterations) and time to get optimal solution. The execution time to find the optimal solution also depends upon the niching-technique applied to evolving population. This paper provides the information about how various authors, researchers, scientists have implemented G...

A Genetic Algorithm (GA) is the process of constructing an optimization problem in which several objectives can be optimized at the same time. In this paper, Strength Pareto Evolutionary Algorithm (SPEA), a GA based multi-objective optimization technique, has been applied to a graph drawing (GD) problem. In this paper a measure (force equalization) which contributes to production of nicely draw...

Recently, genetic algorithms (GA) and particle swarm optimization (PSO) technique have attracted considerable attention among various modern heuristic optimization techniques. Since the two approaches are supposed to find a solution to a given objective function but employ different strategies and computational effort, it is appropriate to compare their performance. This paper presents the appl...

1. Abstract Complex and computationally intensive modeling and simulation of real-world engineering systems can include a large number of design variables in the optimization of such systems. Consequently, it is desirable to conduct variable screening to identify significant or active variables so that a simpler, more efficient, and accurate optimization process can be achieved. This paper empl...

The numerical simulation of the optimal design gravity dams is computationally expensive. Therefore, a new optimization procedure presented in this study to reduce computational cost for determining shape dam. Optimization was performed using combination genetic algorithm (GA) and an updated Kriging surrogate model (UKSM). First, (KSM) constructed with small sample set. Second, minimizing predi...

In this article, by Partitioning of designing space, optimization speed is tried to be increased by GA. To this end, designing space search is done in two steps which are global search and local search. To achieve this goal, according to meshing in FEM, firstly, the list of sections is divided to specific subsets. Then, intermediate member of each subset, as representative of subset, is defined...

We propose an optimization algorithm, called the small-world algorithm (SWA), based on searching mechanisms in social networks. The SWA emphasizes local rather than global search to find the solutions for optimization problems. We investigate two encoding strategies, binary encoding and decimal encoding, in the SWA and test them by function optimization and 01 multidimensional knapsack problems...