TY - JOUR
T1 - Evolutionary computation meets machine learning
T2 - A survey
AU - Zhang, Jun
AU - Zhang, Zhi Hui
AU - Lin, Ying
AU - Chen, Ni
AU - Gong, Yue Jiao
AU - Zhong, Jing Hui
AU - Chung, Henry S.H.
AU - Li, Yun
AU - Shi, Yu Hui
N1 - Funding Information:
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.
PY - 2011/11
Y1 - 2011/11
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/80054958010
U2 - 10.1109/MCI.2011.942584
DO - 10.1109/MCI.2011.942584
M3 - Review article
AN - SCOPUS:80054958010
SN - 1556-603X
VL - 6
SP - 68
EP - 75
JO - IEEE Computational Intelligence Magazine
JF - IEEE Computational Intelligence Magazine
IS - 4
M1 - 6052374
ER -