By Shangce Gao, Hongjian Chai, Beibei Chen, Gang Yang (auth.), Ying Tan, Yuhui Shi, Hongwei Mo (eds.)
This booklet and its better half quantity, LNCS vols. 7928 and 7929 represent the complaints of the 4th foreign convention on Swarm Intelligence, ICSI 2013, held in Harbin, China in June 2013. The 129 revised complete papers offered have been conscientiously reviewed and chosen from 268 submissions. The papers are prepared in 22 cohesive sections protecting all significant subject matters of swarm intelligence study and advancements. the themes lined during this quantity are: hybrid algorithms, swarm-robot and multi-agent platforms, aid vector machines, facts mining equipment, method and data protection, clever regulate, instant sensor community, scheduling and direction making plans, photograph and video processing, and different applications.
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Extra resources for Advances in Swarm Intelligence: 4th International Conference, ICSI 2013, Harbin, China, June 12-15, 2013, Proceedings, Part II
The best-so-far individual). This result may be a solution (or an approximate solution) to the problem. From the above description GP and PSO, the main ideas of two algorithms can be summarized as follows: (a). Decide the encoding way of individuals via problem; (b). Create individuals in initial population; 14 F. Qi et al. (c). Evaluate the population; (d). Obtain evolution message by a certain strategy and generate the new population; (e). Goto step (c) until some stopping conditions satisﬁed; Clearly, step (a) and (d) is the main diﬀerence between GP and PSO.
M. ) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 1082–1090. Springer, Heidelberg (2006) 6. : A cultural algorithm for solving the set covering problem. , Pedrycz, W. ) Analysis and Design of Intelligent Systems using Soft Computing Techniques. ASC, vol. 41, pp. 408–415. Springer, Heidelberg (2007) 7. : A probabilistic heuristic for a computationally diﬃcult set covering problem. Operations Research Letters 8, 67–71 (1989) 8. : Experimental analysis of approximation algorithms for the vertex cover and set covering problems.
In order to ﬁnd a new way for improving the performance of genetic programming, this paper attempts to introduce and modify PSO algorithm into genetic programming. Based on this idea, we propose a hybrid genetic programming with particle swarm optimization (HGPPSO), applied for controlling and evolving populations in each generations. The paper is organized as follows, a brief introduction on genetic programming and particle swarm optimization is showed in Section 2. Section 3 gives the details of the proposed hybrid algorithm.