Knowledge Migration Based Multi-population Cultural Algorithm

Guo, Yinan; Cao, Yuanyuan; Yong, Lin; Wang, Hui

doi:10.1109/icnc.2009.597

Cited by 5 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To improve the performance of CAs in multi-optimal or multi-objective scenarios, the multi-population CA was proposed where individuals from different populations can be exchanged [10]. In 2009, Guo et al presented a scheme called knowledge migration to make populations exchange information much more efficiently and effectively [11]. In the meantime, CAs have been widely applied in optimization [12], neural networks [13], data mining [14] and industry [15].…”

Section: B Developmentmentioning

confidence: 99%

GPU-Accelerated Standard and Multi-population Cultural Algorithms

Dong

Yuan

2013

2013 International Conference on Service Sciences (ICSS)

View full text Add to dashboard Cite

In this paper, we present three parallel cultural algorithms using CUDA-enabled GPUs. Firstly, we used the GPU to accelerate an expensive fitness function. Next, the parallel versions of both standard and multi-population CAs were presented. Experiments show that the standard CA with an expensive fitness function was made more than 600 times faster. On lightweight benchmark problems, the speedups were only 3-4 times for the standard CA while the multi-population CA can still achieve 30-50 times speedups.

show abstract

Section: B Developmentmentioning

confidence: 99%

GPU-Accelerated Standard and Multi-population Cultural Algorithms

Dong

Yuan

2013

2013 International Conference on Service Sciences (ICSS)

View full text Add to dashboard Cite

show abstract

“…And they are marked with multicast IP [5]. According to the principle of knowledge migration [6], similar information may be resonated. Assume A, B, C, and D respectively express four gene units of individuals.…”

Section: Parallel Igas Systemmentioning

confidence: 99%

User’s Preference Aggregation Based on Parallel Interactive Genetic Algorithms

Guo

Yong

Yang

et al. 2010

AMM

Self Cite

View full text Add to dashboard Cite

In traditional interactive genetic algorithms, high-quality optimal solution is hard to be obtained due to small population size and limited evolutional generations. Aming at above problems, a parallel interactive genetic algorithm based on knowledge migration is proposed. During the evolution, the number of the populations is more than one. Evolution information can be exchanged between every two populations so as to guide themselves evolution. In order to realize the freedom communication, IP multicast is adopted as the transfer protocol to find out the similar users instead of traditional TCP/IP communication mode. Taken the fashion evolutionary design system as test platform, the results indicate that the IP multicast-based parallel interactive genetic algorithm has better population diversity. It also can alleviate user fatigue and speed up the convergence.

show abstract

“…Above algorithms are not fully extract and use the evolution information.Coello [4] proposes multi-objective cultural algorithm,which uses implicit knowledge to influence selection opertion so as to keep the distribution of solutions. This method focus on multi-objective cultural algorithms with single-population.The researches about how to use knowledge in multi-population multi-objective cultural algorithms are not taken into account.It has been proved that knowledge migration strategy aiming at single-objective multi-popultion cultural algorithm [5] has better performce than individual migration strategy.So this strategy is extended to MOP.A multi-population multi-objective cultural algorithm adopting knowledge migration(MMOCA) is put forward.…”

Section: Introductionmentioning

confidence: 99%

Multi-Population Multi-Objective Cultural Algorithm

Guo

Cao

Liu

2010

AMR

View full text Add to dashboard Cite

In existing multi-population multi-objective cultural algorithms, information are exchanged among sub-populations by individuals. However, migrated individuals can not reflect the evolution information enough, which limits the evolution performance.In order to enhance the migration efficiency, a novel multi-population multi-objective cultural algorithm adopting knowledge migration is proposed. Implicit knowledge extracted from the evolution process of each sub-population directly reflects the information about dominant search space. By migrating the knowledge among sub-populations at the constant interval, the algorithm realizes more effective interaction with less communication cost. Taken benchmark functions as the examples, simulation results indicate that the algorithm can effectively obtain the Pareto-optimal sets of multi-objective optimization problems. The distribution performance is also improved.

show abstract

Knowledge Migration Based Multi-population Cultural Algorithm

Cited by 5 publications

References 7 publications

GPU-Accelerated Standard and Multi-population Cultural Algorithms

GPU-Accelerated Standard and Multi-population Cultural Algorithms

User’s Preference Aggregation Based on Parallel Interactive Genetic Algorithms

Multi-Population Multi-Objective Cultural Algorithm

Contact Info

Product

Resources

About