A high–efficient multi–deme genetic algorithm with better load–balance. (2018)

• Record Type: Journal Article
• Authors: Jie, Wang; Jiangjun, Yuan
• Is Part Of: International journal of computing science and mathematics. Volume 9:Number 3(2018)
• Page Start: 240
• Page End: 246
• Publication Date: 2018
• Subjects: genetic algorithm -- multi–deme genetic algorithm -- MGA -- load imbalance -- fine–grained schedule; Mathematics -- Periodicals; Computer science -- Periodicals; Mathematics -- Data processing -- Periodicals; 510.285
• Languages: English
• ISSNs: 1752-5055

Abstract

Genetic algorithm is a very powerful search algorithm that fits for many complex situations. However, it is very time consuming, which limits its usage. Previous work which makes use of multi–core systems to parallelise it performs well and gains much attention. This paper introduces that the load–imbalance problem in parallel genetic algorithm will incur large overhead and will limit the performance. We propose two efficient mechanisms (postponed waiting and work stealing) to achieve fine–grained schedule to solve the problem. Compared with traditional multi–deme parallel genetic algorithm, our high–efficient multi–deme genetic algorithm (HMGA) can achieve an average speedup of 1.36.