Abstract
In this paper, a new approach to optimization problems based on the self-organizing feature maps is proposed. We name the new optimization algorithm the SOM-based optimization (SOMO) algorithm. Through the self-organizing process, good solutions to an optimization problem can be simultaneously explored and exploited. An additional advantage of the algorithm is that the outputs of the neural network allow us to transform a multi-dimensional fitness landscape into a three-dimensional projected fitness landscape. Several simulations are used to illustrate the effectiveness of the proposed optimization algorithm.
| Original language | English |
|---|---|
| Pages (from-to) | 781-786 |
| Number of pages | 6 |
| Journal | IEEE International Conference on Neural Networks - Conference Proceedings |
| Volume | 1 |
| State | Published - 2004 |
| Event | 2004 IEEE International Joint Conference on Neural Networks - Proceedings - Budapest, Hungary Duration: 25 Jul 2004 → 29 Jul 2004 |
Keywords
- Evolutionary computing
- Genetic algorithm
- Optimization
- Self-organizing feature map