Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Journal of Global Optimization
Java Concurrency in Practice
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
The Art of Concurrency: A Thread Monkey's Guide to Writing Parallel Applications
The Art of Concurrency: A Thread Monkey's Guide to Writing Parallel Applications
Fast parallelization of differential evolution algorithm using MapReduce
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Differential Evolution: A Survey of the State-of-the-Art
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Adapting evolutionary algorithms to the concurrent functional language Erlang
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Hi-index | 0.00 |
In order to utilize multi-core CPUs more effectively, a new Concurrent Differential Evolution (CDE) is proposed. Then the proposed CDE (CDE/G) is compared with a conventional CDE (CDE/S). CDE/S uses only one population because it is based on the steady-state model. Therefore, CDE/S requires a time-consuming mutual exclusion or "lock" for every read-write access to the population. On the other hand, CDE/G is based on the generational model. By using a secondary population in addition to a primary one, CDE/G does not require any lock on the population and therefore is faster. Through the numerical experiment and the statistical test, it is demonstrated that CDE/G is superior to CDE/S in not only the run-time but also the quality of solutions.