Enhanced multiobjective population-based incremental learning with applications in risk treaty optimization

Author:

Carmona Cortes Omar AndresORCID,Rau-Chaplin Andrew

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Vision and Pattern Recognition,Mathematics (miscellaneous)

Reference22 articles.

1. Alba E (2002) Parallel evolutionary algorithms can achieve super-linear performance. Inf Process Lett 82(1):7–13

2. Baluja S (1994) Population-based incremental learning: a method for integrating genetic search based function optimization and competitive learning. Tech Rep, Pittsburgh

3. Benfield AON (2014) Annual global climate and catastrophe report, impact forecasting. http://thoughtleadership.aonbenfield.com/Documents/20140113 . Accessed 13 Apr 2014

4. Brown L, Beria AA, Cortes O, Rau-Chaplin A, Wilson D, Burke N, Gaiser-Porter J (2014) Parallel MO-PBIL: computing pareto optimal frontiers efficiently with applications in reinsurance analytics. In: Conference on high performance computing simulation (HPCS), 2014 International, pp 766–775

5. Bureerat S (2011) Improved population-based incremental learning in continuous spaces. Soft Comput Ind Appl 96:77–86

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