Recently, on the 27th International Conference on Tools with Artificial Intelligence (ICTAI 2015), the paper, Targeted Mutation: A novel Mutation Strategy for Differential Evolution, published by vice professor Haohuan Fu’s research group won ICTAI-2015 CV Ramamoorthy Best Paper Award.
Differential Evolution (DE) has been shown as an effective, efficient and robust evolutionary computing algorithm. The main force to generate promising offspring is the mutation operator. Usually, two randomly selected vectors are used to generate the differential vector, which maintains the large diversity of mutant directions and ensures the possibility to find global optima. However, strong randomness also leads to the ineffective searching and slow convergence speed. A proper degree of certainty in differential vector will help the population evolve efficiently. This paper proposes a novel mutation strategy called Targeted Mutation that takes the determined target vector as the starting point of the differential vector and maintains the randomness of the ending point, which makes a better trade-off between the certainty and randomness in the differential vector. Besides, Targeted Mutation adopts the best vector as the base vector. The extensive experiments of comparison with two popular mutation operators on 20 benchmark functions demonstrate the competitive performance of our proposed targeted mutation scheme. Our method achieves better or equivalent performance over 70% of total benchmarks against the other two methods. 17 out of 20 function results can get further improved when roughly tuning parameters on each function, showing the potential ability to get even better results. In addition, an integrated evaluation scoring scheme is designed to provide a more concrete demonstration of the overall performance of different approaches, and our method gains the highest score.
The first author of the paper is Weijie Zheng, a PhD candidate of vice professor Haohuan Fu and professor Guangwen Yang.
The main idea of this paper is to reduce the randomness of the differential vector which controls the searching direction of DE, and propose a novel mutation strategy.
The annual IEEE International Conference on Tools with Artificial Intelligence (ICTAI) provides a major international forum where the creation and exchange of ideas related to artificial intelligence are fostered among academia, industry, and government agencies. The conference facilitates the cross-fertilization of these ideas and promotes their transfer into practical tools, for developing intelligent systems and pursuing artificial intelligence applications. The committee chose 3 of all 84 accepted full papers (The acceptance of the full paper is 30%) as Best Papers. This paper is the only one from Chinese research institute.