论文信息 - Parallel evolutionary programming

Parallel evolutionary programming

This work presents a study of parallel evolutionary programming (EP). The paper is divided into two parts. The first part proposes a concept of parallel EP. Four numerical functions are used to compare the performance between the serial algorithm and the parallel algorithm. In the second part, we apply parallel EP to a more complicated problem - an evolving neural networks problem. The results from this problem show that the parallel version is not only faster than the serial version, but the parallel version also more reliably finds optimal solutions.

Xin Yao | Shisanu Tongchim

[1] Risto Miikkulainen,et al. Evolving Neural Networks through Augmenting Topologies , 2002, Evolutionary Computation.

[2] Xin Yao,et al. PEPNet: Parallel Evolutionary Programming for Constructing Artificial Neural Networks , 1997, Evolutionary Programming.

[3] Xin Yao,et al. A new evolutionary system for evolving artificial neural networks , 1997, IEEE Trans. Neural Networks.

[4] Enrique Alba,et al. A survey of parallel distributed genetic algorithms , 1999, Complex..

[5] Enrique Alba,et al. A survey of parallel distributed genetic algorithms , 1999 .

[6] Francisco Herrera,et al. Gradual distributed real-coded genetic algorithms , 2000, IEEE Trans. Evol. Comput..

[7] Xin Yao,et al. Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

[8] Erick Cantú-Paz,et al. A Survey of Parallel Genetic Algorithms , 2000 .