Papers by Author: Qu Li

Abstract: Evolutionary Multi-objective Optimization (EMO) is a hot research direction nowadays and one of the state-of-the-art evolutionary multi-objective optimization algorithms ——NSGA-II has gain wide attention and application in many fields. Gene Expression Programming (GEP) has a powerful search capability, but falls into local optimum easily. Based on the transformed GEP, NSGA-II and the virus evolution mechanism, a new multi-objective evolutionary algorithm GEP Virus NSGA-II is proposed. With the infection operation of virus population, the diversity of the host population is increased, and it’s much easier to jump out of the local optimum. And this algorithm has got good experimental results on 9 standard test problems.

Abstract: Gene Expression Programming(GEP) is a novel and accurate approach for classification. With the shortcoming of GEP, it often falls into the local optimums. In this paper, we introduce the virus evolutionary mechanism into GEP, with the infection operation of virus population, the diversity of the host population is increased, and the system is much easier to jump out of the local optimums, and much faster to obtain better results. Experiments on several benchmark data sets show that our approach can get close average accuracy and much better best accuracy compared with available results. What’s more, the average execution time is largely decreased due to smaller population size and maximum generation.

Abstract: Gene expression programming (GEP) is a kind of phenotype/genotype based evolutionary computation. Code reuse is an important issue in GEP. Various methods are used in current literature to achieve this task. In this paper, we compared six GEP based algorithms by experiments. We proved that although it’s possible invent different kinds of code reuse strategies, current available strategies are powerful and efficient.