Title :
Research on Self-Adaptive Float Evolution Algorithm Based on DE
Author_Institution :
Sch. of Inf., Henan Univ. of Finance & Econ., Zhengzhou
Abstract :
Since differential evolution (DE) proposed by researchers, it has been being the focus researched in evolutionary computation. There are better search performance, stronger robustness and higher perturbation property in DE. In improving the performance of algorithm and running quality, extending evolution algorithm application to engineering optimization, float code is superior to other codes. In this paper, DE was combined with float code evolutionary computation, self-adaptive float evolution algorithms based on differential evolution (SFEADE) was presented. Through analyzing and experiment, the result indicated the algorithm was credible in performance and feasible in method. It was stronger in practicability.
Keywords :
evolutionary computation; optimisation; engineering optimization; evolutionary computation; perturbation property; search performance; self-adaptive float evolution algorithms based on differential evolution; Automation; Clustering algorithms; Evolutionary computation; Filtering theory; Filters; Finance; Genetics; Pareto optimization; Performance analysis; Robustness; Differential Evolution; Evolution Algorithm; Float Code; Self Adaptive;
Conference_Titel :
Intelligent Computation Technology and Automation (ICICTA), 2008 International Conference on
Conference_Location :
Hunan
Print_ISBN :
978-0-7695-3357-5
DOI :
10.1109/ICICTA.2008.158