Abstract
Evolutionary computation (EC) is a kind of optimization methodology inspired by the mechanisms of biological evolution and behaviors of living organisms. SI algorithms share many common characteristics with EAs and are also regarded to be in the EC algorithm family. The new population is then evaluated again and the iteration continues until a termination criterion is satisfied. ML is one of the most promising and salient research areas in artificial intelligence, which has experienced a rapid development and has become a powerful tool in a wide range of applications. In many applications, EC algorithms incorporating ML techniques have been proven to be advantageous in both convergence speed and solution quality. The survey is organized from the EC perspective, including population initialization, fitness evaluation and selection, population reproduction and variation, algorithm adaptation, and local search.
| Original language | English |
|---|---|
| Article number | 6052374 |
| Pages (from-to) | 68-75 |
| Number of pages | 8 |
| Journal | IEEE Computational Intelligence Magazine |
| Volume | 6 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 20 Oct 2011 |
Funding
This work was supported in part by the National Natural Science Foundation of China (NSFC) No.61070004, by NSFC Joint Fund with Guangdong under Key Project U0835002, and by the National High-Technology Research and Development Program (“863” Program) of China No. 2009AA01Z208.
Keywords
- data analysis
- evolutionary computation
- iterative methods
- learning (artificial intelligence)
- search problems