MAS-Bench: Parameter Optimization Benchmark for Multi-agent Crowd Simulation

[1]  Hiroshi Sawada,et al.  Theme Park Simulation based on Questionnaires for Maximizing Visitor Surplus , 2020, AAMAS.

[2]  Arthur C. Sanderson,et al.  JADE: Adaptive Differential Evolution With Optional External Archive , 2009, IEEE Transactions on Evolutionary Computation.

[3]  Takuya Akiba,et al.  Optuna: A Next-generation Hyperparameter Optimization Framework , 2019, KDD.

[4]  Chris Eliasmith,et al.  Hyperopt: a Python library for model selection and hyperparameter optimization , 2015 .

[5]  Yoshua Bengio,et al.  Algorithms for Hyper-Parameter Optimization , 2011, NIPS.

[6]  Dinesh Manocha,et al.  Parameter estimation and comparative evaluation of crowd simulations , 2014, Comput. Graph. Forum.

[7]  Wael K.M. Alhajyaseen,et al.  Quality of pedestrian flow and crosswalk width at signalized intersections , 2010 .

[8]  Mohammed El-Abd,et al.  Black-box optimization benchmarking for noiseless function testbed using particle swarm optimization , 2009, GECCO '09.

[9]  Alex S. Fukunaga,et al.  Success-history based parameter adaptation for Differential Evolution , 2013, 2013 IEEE Congress on Evolutionary Computation.

[10]  Nikolaus Hansen,et al.  Evaluating the CMA Evolution Strategy on Multimodal Test Functions , 2004, PPSN.

[11]  Yoshua Bengio,et al.  Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..

[12]  Anne Auger,et al.  Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009 , 2010, GECCO '10.

[13]  Fangkai Yang,et al.  Priority driven Local Optimization for Crowd Simulation , 2019, AAMAS.

[14]  L. Darrell Whitley,et al.  The dispersion metric and the CMA evolution strategy , 2006, GECCO.