By Zhuo Kang, Lishan Kang, Xiufen Zou, Minzhong Liu, Changhe Li, Ming Yang, Yan Li (auth.), Lishan Kang, Yong Liu, Sanyou Zeng (eds.)
This ebook constitutes the refereed lawsuits of the second one foreign Symposium on Intelligence Computation and functions, ISICA 2007, held in Wuhan, China, in September 2007. The seventy one revised complete papers have been conscientiously reviewed and chosen from approximately one thousand submissions. the themes comprise evolutionary computation, evolutionary studying, neural networks, swarms, trend attractiveness, info mining and others.
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Extra resources for Advances in Computation and Intelligence: Second International Symposium, ISICA 2007 Wuhan, China, September 21-23, 2007 Proceedings
Step 5: Perform binary tournament selection on Pt+1. Step 6: Apply the recombination and mutation operation on Pt+1, and copy Pt+1 to Rt+1. Step 7: Increment generation counter, or let t=t+1; and then go to step 2. Discussions about Pruning Procedure: In our research, all non-dominated individuals are copied to Pt+1 when they are found from the environmental selection population Rt. The set Pt+1 must be reduced when its size exceeds a specified value N in step 3. This process is called pruning procedure, and the pruning procedure is very important to make the MOEAs perform well on diversity of solutions.
We add an equivalent energy component in the launch velocity to take into account of not colliding with the swing-by planet. 3 Handling Using NSGA-II First, we discuss the representation scheme for the decision variables within the NSGAII framework . We fix a maximum of three swing-by planets, thereby leaving us with four options: (i) direct flight (no swing-by), (ii) one planet swing-by, (iii) two planet swing-by and (iv) three planet swing-by. We use a two-bit substring for representing these four options with 00, 01, 10 and 11, respectively.
Fundamental properties of optimal orbital transfers. In: 54th International Astronautical Congress, pp. in Abstract. In this paper, we propose a hybrid reference-point based evolutionary multi-objective optimization (EMO) algorithm coupled with the classical SQP procedure for solving constrained single-objective optimization problems. The reference point based EMO procedure allows the procedure to focus its search near the constraint boundaries, while the SQP methodology acts as a local search to improve the solutions.