SAT problem is the first proved NP-complete problems. Heuristic methods on solving the SAT problem although belongs to incomplete method, but it has its advantages. geneticalgorithm (GA) as one of the heuristic algor...
详细信息
ISBN:
(纸本)9781538618295
SAT problem is the first proved NP-complete problems. Heuristic methods on solving the SAT problem although belongs to incomplete method, but it has its advantages. geneticalgorithm (GA) as one of the heuristic algorithms, was applied to solve the SAT problem of many years, and also got some better results combine with other algorithms. However, there is still room for improvement. In this paper we combine GA with the Local Search algorithm (LSA) and improve the sort algorithm. Using the Open MP to implement the parallelhybrid GA based on the Coarse-Grained Model (CGPHGA). This article describes the design and implementation of CGPHGA in detail, According to the experimental results, CGPHGA improves the success rate and efficiency.
A novel phase retrieval method for hard X-ray inline phase contrast imaging is presented in this paper, which is based on a parallelhybridgeneticalgorithm. Different from the tradition numerical algorithm, our ...
详细信息
A novel phase retrieval method for hard X-ray inline phase contrast imaging is presented in this paper, which is based on a parallelhybridgeneticalgorithm. Different from the tradition numerical algorithm, our approach is a parallelhybrid intelligent method. Some techniques, such as parallel computing, simulated annealing selection and elitist migration, are all combined together with the geneticalgorithm to accelerate the convergence and improve the quality of the final output. The proposed method is validated by numerical simulations on MATLAB platform. Experimental results show that the parallelhybrid GA is a satisfying solution for phase retrieval because of its fast convergence and high speed-up.
Workforce planning is an important activity that enables organizations to determine the workforce needed for continued success. A workforce planning problem is a very complex task requiring modern techniques to be sol...
详细信息
An important and challenging data mining application in marketing is to learn models for predicting potential customers who contribute large profit to a company under resource constraints. In this paper, we first form...
详细信息
ISBN:
(纸本)9781424481262
An important and challenging data mining application in marketing is to learn models for predicting potential customers who contribute large profit to a company under resource constraints. In this paper, we first formulate this learning problem as a constrained optimization problem and then converse it to an unconstrained Multi-objective Optimization Problem (MOP). A parallel Multi-Objective Evolutionary algorithm (MOEA) on consumer-level graphics hardware is used to handle the MOP. We perform experiments on a real-life direct marketing problem to compare the proposed method with the parallel hybrid genetic algorithm, the DMAX approach, and a sequential MOEA. It is observed that the proposed method is much more effective and efficient than the other approaches.
暂无评论