Abstract
Searching neural networks' (NN) optimal parameters can be seen as a multi-modal optimization problem. This paper proposes a memetic water wave optimization algorithm (WWOMA) to determine the optimal weights of NN. In the proposed WWO-based MA (WWOMA), we employ WWO to perform global search by both individual improvement and population co-evolution, and then employ several local search components to enhance its local refinement ability. Furthermore, a Meta-Lamarckian learning strategy is utilized to choose a proper local refinement component to concentrate computational efforts on more promising solutions. We carry out numerical experiments on six well-known NN designing benchmark problems, the empirical results demonstrate both the feasibility and effectiveness by applying WWOMA to design NN.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.