A highefficient multideme genetic algorithm with better loadbalance. (2018) - Jie, Wang; Jiangjun, Yuan
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 multicore systems to parallelise it performs well and gains much attention. This paper introduces that the loadimbalance 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 finegrained schedule to solve the problem. Compared with traditional multideme parallel genetic algorithm, our highefficient multideme genetic algorithm (HMGA) can achieve an average speedup of 1.36.