Download Multi-Agent Applications with Evolutionary Computation and by Shu-Heng Chen, Yasushi Kambayashi, Hiroshi Sato PDF

By Shu-Heng Chen, Yasushi Kambayashi, Hiroshi Sato

Biologically encouraged computation tools are transforming into in acceptance in clever platforms, making a want for extra examine and information.Multi-Agent functions with Evolutionary Computation and Biologically encouraged applied sciences: clever innovations for Ubiquity and Optimization compiles a number of ongoing initiatives and study efforts within the layout of brokers in mild of modern improvement in neurocognitive technology and quantum physics. This cutting edge assortment presents readers with interdisciplinary purposes of multi-agents structures, starting from economics to engineering.

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Extra resources for Multi-Agent Applications with Evolutionary Computation and Biologically Inspired Technologies: Intelligent Techniques for Ubiquity and Optimization

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86. , difference is zero or the hypothesis that there is no difference between the two forecasts) cannot be rejected at any reasonable level of significance. Plot of actual materialized LXXR against that of the best forecast April 2008 through March 2009 are in Figure 6. CONCLUSION This chapter introduced novel thought on utilizing agent-based modeling to forecast the Los Angeles metropolitan area S&P/Case-Shiller Index for the 24 months: April 2008 through March 2010. The construction of agent-based modeling was based on progression defined in three stages.

TSGP is used because it is designed specifically to produce regressiontype models and to compute standard regression statistics. Statistical properties of models TSGP produces were analyzed in Kaboudan (2001). Two types of input files are needed for executing TSGP: data files and a configuration file. Data input files contain values of the dependent and each of the independent variables. The configuration file contains execution information a user controls. TSGP assembles an initial population of individual equations (say 1000 of them) with random specifications, computes their fitness (MSE = mean squared error), and then breeds new equations as members of a new generation with the same population size.

2001). Genetically evolved models and normality of their residuals. Journal of Economic Dynamics & Control, 25, 1719–1749. 1016/S0165-1889(00)00004-X Kaboudan, M. (2004). TSGP: A time series genetic programming software. edu/ fac/ mak_kaboudan/tsgp. Koza, J. (1992). Genetic programming. Cambridge, MA: The MIT Press. , & Torsten, S. (2001). The impact of stock prices and house prices on consumption in OECD countries. pdf. Money, C. N. N. com (2008). S. housing and financial sectors. ap/. Moody’s.

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