By Seyed Naser Razavi, Nicolas Gaud, Abderrafiâa Koukam, Naser Mozayani (auth.), Ying Tan, Yuhui Shi, Zhen Ji (eds.)
This e-book and its significant other quantity, LNCS vols. 7331 and 7332, represent the court cases of the 3rd overseas convention on Swarm Intelligence, ICSI 2012, held in Shenzhen, China in June 2012. The one hundred forty five complete papers provided have been conscientiously reviewed and chosen from 247 submissions. The papers are equipped in 27 cohesive sections overlaying all significant themes of swarm intelligence examine and developments.
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Table 1. 73 16 C. Liu et al. Table 2. Simulation study(sample size of 300). Frequency of variable selection (LASSO: Lasso method; ALASSO: adaptive Lasso method; L1/2: L1/2 regularization method ; CR: Censoring rate). The data is from the Mayo Clinic trial in primary biliary cirrhosis (PBC) of the liver conducted between 1974 and 1984. In practices, we also restrict our attention to the 276 observations without missing values. Table 3. Primary biliary cirrhosis data. Estimate coefficients by Lasso, adaptive Lasso and methods.
Basic components of the GP system Objective Terminal Set Finding the expansions for a given kernel function Complex variables , y, ; integer variable ; and random constants chosen from [-5, 5] +, -, , /, exp, pow, log, factorial Sum of the squared errors over 100 fitness cases Tournament selection with tournament size 50 Ramped half-and-half (depth 0 to 4) Size-fair crossover A combination of size-fair mutations See Table 2 Finding a solution with a total error less than 10-5 Anti-bloat selection and anti-bloat genetic operators Function Set Fitness Selection Initialization Crossover Mutation Parameters Termination Bloat Table 2.
Springer, Berlin (1988) 4. : The top ten algorithms of the century 2(1), 22–23 (2000) 5. : A rapid hierarchical radiosity algorithm. In: SIGGRAPH (1991) 6. : Load balancing and data locality in hierarchical N-body methods. Journal of Parallel and Distributed Computing (1992) 7. : Multi-agent based simulations using fast multipole method: application to large scale simulations of flocking dynamical systems. Artificial Intelligence Review 35(1), 53–72 (2011) 8. : Automatic Dynamics Simplification in Fast Multipole Method: Application to Large Flocking Systems.
Advances in Swarm Intelligence: Third International Conference, ICSI 2012, Shenzhen, China, June 17-20, 2012 Proceedings, Part II by Seyed Naser Razavi, Nicolas Gaud, Abderrafiâa Koukam, Naser Mozayani (auth.), Ying Tan, Yuhui Shi, Zhen Ji (eds.)