Accelerated genetic algorithms : combined with local search techniques for fast and accurate global search
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Detail(s)
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the IEEE Conference on Evolutionary Computation |
| Pages | 378-383 |
| Volume | 1 |
| Publication status | Published - 1995 |
| Externally published | Yes |
Publication series
| Name | |
|---|---|
| Volume | 1 |
Conference
| Title | 1995 IEEE International Conference on Evolutionary Computation |
|---|---|
| Place | Australia |
| City | Perth |
| Period | 29 November - 1 December 1995 |
Link(s)
Abstract
This paper discusses a combinatorial method to compute a global solution of a non-convex optimization problem. A hybrid algorithm is the synthesis of a genetic algorithm and a local search algorithm (e.g nonlinear programming). The hybrid algorithm is not only faster than the genetic algorithm but also gives a more accurate solution. In addition, the length of chromosome required is much smaller. An abstract analysis of the hybrid algorithm is discussed based on which a convergent proof of this algorithm is derived. Finally, simulations are performed to show the efficiency of the algorithm.
Citation Format(s)
Accelerated genetic algorithms: combined with local search techniques for fast and accurate global search. / Chak, Chu Kwong; Feng, Gang.
Proceedings of the IEEE Conference on Evolutionary Computation. Vol. 1 1995. p. 378-383.
Proceedings of the IEEE Conference on Evolutionary Computation. Vol. 1 1995. p. 378-383.
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
