Improved incremental extreme learning machine based on multi-learning clonal selection algorithm
WANG Chao
WANG Jian-hui
GU Shu-sheng
WANG Xiao
ZHANG Yu-xian
Abstract：The great number of redundant nodes in an incremental extreme learning machine (I–ELM) may lower the learning efficiency of the algorithm, and complicate the network structure. To deal with this problem, we propose the improved I–ELM with kernel (I–ELMK) on the basis of multi-learning clonal selection algorithm (MLCSA). The MLCSA uses Baldwinian learning and Lamarckian learning, to exploit the search space by employing the information of antibodies, and reinforce the exploitation capacity of individual information. The proposed algorithm can limit the number of hidden layer neurons effectively to obtain more compact network architecture. The simulations show that MLCSI–ELMK has higher prediction accuracies online and off-line, while providing a better capacity of generalization compared with other algorithms.
Keywords：clonal selection algorithmBaldwinian learningLamarckian learningneural networksincremental ex-treme learning machinesoft computing
Publication Date：2016-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：12( 368-379 )