Random optimization
From Wikipedia, the free encyclopedia
Random optimization is the name applied to a class of algorithms which can be used to solve optimization problems.
Random optimization is relatively little known, but can be compared with genetic algorithms, and often random optimization outperforms other methods with significantly faster convergence.
Contrast Random-restart hill climbing; genetic algorithm.
[edit] References
- Baba, N (1989) A new approach for finding the global minimum of error functions of neural networks, Neural Networks, vol 2, pp 367-373
- Matyas, J (1965), Random optimization, Automation and remote control, vol 26, pp 246-253
- Solis, F.J and Wets, R.J (1981), Minimization by random search techniques, Mathematics of operations research, vol 6, no 1, pp 19-30
