Abstract
The paper describes a new evolutionary system for evolving artificial neural networks (ANN’s) called PSONN, which is based on the particle swarm optimisation (PSO) algorithm. The PSO algorithm is used to evolve both the architecture and weights of ANN’s, this means that an ANN’s architecture is adaptively adjusted by PSO algorithm, then the nodes of this ANN’s are also evolved by PSO algorithm to evaluate the quality of this network architecture. This process is repeated until the best ANN’s is accepted or the maximum number of generations has been reached. In PSONN, a strategy of evolving added nodes and a partial training algorithm are used to maintain a close behavioural link between the parents and their offspring, which improves the efficiency of evolving ANN’s. PSONN has been tested on two real problems in the medical domain. The results show that ANN’s evolved by PSONN have good accuracy and generalisation ability.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: 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.
