By Shangce Gao, Hongjian Chai, Beibei Chen, Gang Yang (auth.), Ying Tan, Yuhui Shi, Hongwei Mo (eds.)
This e-book and its better half quantity, LNCS vols. 7928 and 7929 represent the lawsuits of the 4th overseas convention on Swarm Intelligence, ICSI 2013, held in Harbin, China in June 2013. The 129 revised complete papers provided have been rigorously reviewed and chosen from 268 submissions. The papers are equipped in 22 cohesive sections overlaying all significant issues of swarm intelligence study and advancements. the themes coated during this quantity are: hybrid algorithms, swarm-robot and multi-agent structures, help vector machines, info mining equipment, procedure and knowledge protection, clever regulate, instant sensor community, scheduling and course making plans, snapshot 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
Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948. Perth (1995) 17. : The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6(1), 58–73 (2002) 18. : The fully informed particle swarm: Simpler, maybe better. IEEE Transactions on Evolutionary Computation 8, 204–210 (2004) 19. : A hierarchical particle swarm optimizer and its adaptive variant. IEEE Transactions on Systems, Man and Cybernetics - Part B 3(6), 1272–1282 (2005) 20.
Simulation results are presented in Section 4. Finally, some conclusions are summarized in Section 5. 2 A Brief Summary for Basic GP and PSO In this paper, the genetic programming and particle swarm optimization which we refer are both their basic forms. In genetic programming, the genetic algorithm operates on a population of computer programs by varying sizes and shapes. Genetic programming starts with thousands or millions of randomly generated computer programs composed of the available programmatic ingredients and then applies the principles in evolution of species to breed an improved population of programs.
By this way, we investigate the ﬂuctuation trend of CT, ACG and ACT. The simulation results are showed in Fig 1. From the above Fig 1, we can see that, with the reduce of the population size, the ﬂuctuation trend of HGPPSO in CT, ACG, ACT changes smoothly and stably. That is to say, HGPPSO can get better performance in a small population size, this is the most signiﬁcant feature of HGPPSO which is useful in solving problems. 5 Conclusions By changing the linear encoding to tree encoding and redeﬁning the evolving rules based on the new encoding, PSO algorithm is introduced into genetic programming and an hybrid genetic programming with particle swarm optimization (HGPPSO) is proposed.
Advances in Swarm Intelligence: 4th International Conference, ICSI 2013, Harbin, China, June 12-15, 2013, Proceedings, Part II by Shangce Gao, Hongjian Chai, Beibei Chen, Gang Yang (auth.), Ying Tan, Yuhui Shi, Hongwei Mo (eds.)