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On the huge benefit of quasi-random mutations for multimodal optimization with application to grid-based tuning of neurocontrollers
Abstract : In this paper, we study the optimization of a neural network used for controlling a Monte-Carlo Tree Search (MCTS/UCT) algorithm. The main results are: (i) the specification of a new multimodal benchmark function; this function has been defined in particular in agreement with [1] which has pointed out that most multimodal functions are not satisfactory for some real-world multimodal scenarios (section 2); (ii) experimentation of Evolution Strategies on this new multimodal benchmark function, showing the great efficiency of quasi-random mutations in this framework (section 3); (iii) the proof-of-concept of the application of ES for grid-based tuning Neural Networks for controlling MCTS/UCT (see section 3).
Cited literature [23 references]
https://hal.inria.fr/inria-00380125
Contributor : Fabien Teytaud <>
Submitted on : Thursday, April 30, 2009 - 9:00:21 AM
Last modification on : Wednesday, September 16, 2020 - 5:06:55 PM
Long-term archiving on: : Thursday, June 10, 2010 - 6:51:53 PM
Contributor : Fabien Teytaud <>
Submitted on : Thursday, April 30, 2009 - 9:00:21 AM
Last modification on : Wednesday, September 16, 2020 - 5:06:55 PM
Long-term archiving on: : Thursday, June 10, 2010 - 6:51:53 PM
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