MCTS guided Genetic Algorithm for optimization of neural network weights

Akshay Hebbar
8/7/2023
cs.NEcs.LG

Official code: AkshayHebbar/MCTS-GA (3 stars, MIT, last commit 2023-05-18).

Abstract

In this research, we investigate the possibility of applying a search strategy to genetic algorithms to explore the entire genetic tree structure. Several methods aid in performing tree searches; however, simpler algorithms such as breadth-first, depth-first, and iterative techniques are computation-heavy and often result in a long execution time. Adversarial techniques are often the preferred mechanism when performing a probabilistic search, yielding optimal results more quickly. The problem we are trying to tackle in this paper is the optimization of neural networks using genetic algorithms. Genetic algorithms (GA) form a tree of possible states and provide a mechanism for rewards via the fitness function. Monte Carlo Tree Search (MCTS) has proven to be an effective tree search strategy given states and rewards; therefore, we will combine these approaches to optimally search for the best result generated with genetic algorithms.

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Code Implementations(1)

AkshayHebbar/MCTS-GAOfficial
MIT

Monte Carlo Tree Search guided Genetic Algorithm for optimization of neural network weights

30Jupyter NotebookMay 18, 20233 years agoMIT
genetic-algorithmmonte-carlo-tree-searchoptimization-algorithmsreinforcement-learning

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Cite this paper

@article{hebbar2023mcts,
  title  = {MCTS guided Genetic Algorithm for optimization of neural network weights},
  author = {Akshay Hebbar},
  year   = {2023},
  eprint = {2308.04459},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2308.04459v1}
}

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