Learn More
Efficient algorithms include the one that finds high quality solutions, with reasonable computational speed. This paper presents an adaptation to a parallel computer architecture based on estimation of distribution and genetic algorithms (EDAs and GAs) hybridization. In this master-slave topology, the master selects portions of the search space, and slaves(More)
Hybrid algorithms incorporated with parallel processing techniques are very powerful tools for efficiently solving very complex optimization problems. We present asynchronous parallel computer architecture adaptation based on hybridization of Genetic Algorithms (GAs) and Estimation of Distribution Algorithms (EDAs). In this master-slave formulation, slaves(More)
This paper adapts parallel master-slave estimation of distribution and genetic algorithms (GAs and EDAs) hybridization. The master selects portions of the search space, and slaves perform, in parallel and independently, a GA that solves the problem on the assigned portion of the search space. The master's work is to progressively narrow the areas explored(More)
This paper proposes a hybrid method of estimation of distribution algorithms (EDAs) and genetic algorithms (GAs) based on master/slave cooperation. The master process estimates the probability distribution of the search space based on the non-dependency model at each iteration and sends probability vectors to slaves. The slaves use the vector to generate(More)
Exploring a solution space in an effective way in any optimization algorithm does not only help to find good quality solutions, but also to reduce overall algorithm's computation time. This work proposes an optimization technique that utilizes hybridization, asynchronous parallel processing and strategic searching, to solve large scale and complex(More)
  • 1