Scheduling variable-length messages in a single-hop multichannel local lightwave network
IEEE/ACM Transactions on Networking (TON)
A Genetic Algorithm for Multiprocessor Scheduling
IEEE Transactions on Parallel and Distributed Systems
A Novel Optical IP Router Architecture for WDM Networks
ICOIN '01 Proceedings of the The 15th International Conference on Information Networking
Heterogeneous Parallelization of the Linkmap Program
ICPP '00 Proceedings of the 2000 International Workshop on Parallel Processing
(R) A Study of a Non-Linear Optimization Problem Using a Distributed Genetic Algorithm
ICPP '96 Proceedings of the Proceedings of the 1996 International Conference on Parallel Processing - Volume 2
An optimization solution for packet scheduling: a pipeline-based genetic algorithm accelerator
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Generating fuzzy rules for target tracking using a steady-stategenetic algorithm
IEEE Transactions on Evolutionary Computation
Search-based software engineering: Trends, techniques and applications
ACM Computing Surveys (CSUR)
| Hi-index | 14.98 |
The meta-heuristic methods, genetic algorithms (GAs), are frequently used to obtain optimal solutions for some complicated problems. However, due to the characteristic of natural evolution, the methods slowly converge the derived solutions to an optimal solution and are usually used to solve complicated and offline problems. While, in a real-world scenario, there are some complicated but real-time problems that require being solved within a short response time and have to obtain an optimal or near optimal solution due to performance considerations. Thus, the convergence speed of GAs becomes an important issue when it is applied to solve time-critical optimization problems. To address this, this paper presents a novel method, named hyper-generation GA (HG-GA), to improve the convergence speed of GAs. The proposed HG-GA breaks the general rule of generation-based evolution and uses a pipeline operation to accelerate the convergence speed of obtaining an optimal solution. Based on an example of a time-critical scheduling process in an optical network, both analysis and simulation results show that the HG-GA can generate more and better chromosomes than general GAs within the same evolutionary period. The rapid convergence property of the HG-GA increases its potential to solve many complicated problems in real-time systems.