scholarly journals A graph-based genetic algorithm and generative model/Monte Carlo tree search for the exploration of chemical space

2019 ◽  
Vol 10 (12) ◽  
pp. 3567-3572 ◽  
Author(s):  
Jan H. Jensen
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Monte Carlo ◽  
Chemical Space ◽  
Generative Model ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
Synthetic Accessibility ◽  
Better Than

This paper presents a comparison of a graph-based genetic algorithm (GB-GA) and machine learning (ML) results for the optimization of log P values with a constraint for synthetic accessibility and shows that the GA is as good as or better than the ML approaches for this particular property.

scholarly journals Graph-based Genetic Algorithm and Generative Model/Monte Carlo Tree Search for the Exploration of Chemical Space

2018 ◽  
Author(s):  
Jan H. Jensen
Keyword(s):  
Genetic Algorithm ◽  
Monte Carlo ◽  
Chemical Space ◽  
Generative Models ◽  
Generative Model ◽  
Small Data ◽  
Data Sets ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
Small Data Sets

This paper presents a comparison of a graph-based genetic algorithm (GB-GA) and machine learning (ML) results for the optimisation of logP values with a constraint for synthetic accessibility and shows that GA is as good or better than the ML approaches for this particular property. The molecules found by GB-GA bear little resemblance to the molecules used to construct the initial mating pool, indicating that the GB-GA approach can traverse a relatively large distance in chemical space using relatively few (50) generations. The paper also introduces a new non-ML graph-based generative model (GB-GM) that can be parameterized using very small data sets and combined with a Monte Carlo tree search (MCTS) algorithm. The results are comparable to previously published results (Sci. Technol. Adv. Mater. 2017, 18, 972-976) using a recurrent neural network (RNN) generative model, while the GB-GM-based method is orders of magnitude faster. The MCTS results seem more dependent on the composition of the training set than the GA approach for this particular property. Our results suggest that the performance of new ML-based generative models should be compared to more traditional, and often simpler, approaches such a GA.

scholarly journals Graph-based Genetic Algorithm and Generative Model/Monte Carlo Tree Search for the Exploration of Chemical Space

2019 ◽  
Author(s):  
Jan H. Jensen
Keyword(s):  
Genetic Algorithm ◽  
Monte Carlo ◽  
Chemical Space ◽  
Generative Models ◽  
Generative Model ◽  
Small Data ◽  
Data Sets ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
Small Data Sets

This paper presents a comparison of a graph-based genetic algorithm (GB-GA) and machine learning (ML) results for the optimisation of logP values with a constraint for synthetic accessibility and shows that GA is as good or better than the ML approaches for this particular property. The molecules found by GB-GA bear little resemblance to the molecules used to construct the initial mating pool, indicating that the GB-GA approach can traverse a relatively large distance in chemical space using relatively few (50) generations. The paper also introduces a new non-ML graph-based generative model (GB-GM) that can be parameterized using very small data sets and combined with a Monte Carlo tree search (MCTS) algorithm. The results are comparable to previously published results (Sci. Technol. Adv. Mater. 2017, 18, 972-976) using a recurrent neural network (RNN) generative model, while the GB-GM-based method is orders of magnitude faster. The MCTS results seem more dependent on the composition of the training set than the GA approach for this particular property. Our results suggest that the performance of new ML-based generative models should be compared to more traditional, and often simpler, approaches such a GA.

scholarly journals Graph-based Genetic Algorithm and Generative Model/Monte Carlo Tree Search for the Exploration of Chemical Space

2019 ◽  
Author(s):  
Jan H. Jensen
Keyword(s):  
Genetic Algorithm ◽  
Monte Carlo ◽  
Chemical Space ◽  
Generative Models ◽  
Generative Model ◽  
Small Data ◽  
Data Sets ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
Small Data Sets

This paper presents a comparison of a graph-based genetic algorithm (GB-GA) and machine learning (ML) results for the optimisation of logP values with a constraint for synthetic accessibility and shows that GA is as good or better than the ML approaches for this particular property. The molecules found by GB-GA bear little resemblance to the molecules used to construct the initial mating pool, indicating that the GB-GA approach can traverse a relatively large distance in chemical space using relatively few (50) generations. The paper also introduces a new non-ML graph-based generative model (GB-GM) that can be parameterized using very small data sets and combined with a Monte Carlo tree search (MCTS) algorithm. The results are comparable to previously published results (Sci. Technol. Adv. Mater. 2017, 18, 972-976) using a recurrent neural network (RNN) generative model, while the GB-GM-based method is orders of magnitude faster. The MCTS results seem more dependent on the composition of the training set than the GA approach for this particular property. Our results suggest that the performance of new ML-based generative models should be compared to more traditional, and often simpler, approaches such a GA.

scholarly journals Industrial scheduling with Monte Carlo tree search and machine learning

Procedia CIRP ◽  
2018 ◽  
Vol 72 ◽  
pp. 1283-1287 ◽  
Author(s):  
Marco Lubosch ◽  
Martin Kunath ◽  
Herwig Winkler
Keyword(s):  
Machine Learning ◽  
Monte Carlo ◽  
Tree Search ◽  
Monte Carlo Tree Search

scholarly journals Subgoal-Based Temporal Abstraction in Monte-Carlo Tree Search

2019 ◽  
Author(s):  
Thomas Gabor ◽  
Jan Peter ◽  
Thomy Phan ◽  
Christian Meyer ◽  
Claudia Linnhoff-Popien
Keyword(s):  
Monte Carlo ◽  
Computational Efficiency ◽  
Generative Model ◽  
Tree Search ◽  
Temporal Abstraction ◽  
Monte Carlo Tree Search

We propose an approach to general subgoal-based temporal abstraction in MCTS. Our approach approximates a set of available macro-actions locally for each state only requiring a generative model and a subgoal predicate. For that, we modify the expansion step of MCTS to automatically discover and optimize macro-actions that lead to subgoals. We empirically evaluate the effectiveness, computational efficiency and robustness of our approach w.r.t. different parameter settings in two benchmark domains and compare the results to standard MCTS without temporal abstraction.

scholarly journals Automated Machine Learning with Monte-Carlo Tree Search

2019 ◽  
Author(s):  
Herilalaina Rakotoarison ◽  
Marc Schoenauer ◽  
Michèle Sebag
Keyword(s):  
Machine Learning ◽  
Monte Carlo ◽  
Search Algorithm ◽  
Search Space ◽  
Bayesian Optimization ◽  
Peak Performance ◽  
Tree Search ◽  
Algorithm Selection ◽  
Monte Carlo Tree Search ◽  
Warm Start

The AutoML approach aims to deliver peak performance from a machine learning  portfolio on the dataset at hand. A Monte-Carlo Tree Search Algorithm Selection and Configuration (Mosaic) approach is presented to tackle this mixed (combinatorial and continuous) expensive optimization problem on the structured search space of ML pipelines. Extensive lesion studies are conducted to independently assess and compare: i) the optimization processes based on Bayesian Optimization or Monte Carlo Tree Search (MCTS); ii) its warm-start initialization based on meta-features or random runs; iii) the ensembling of the solutions gathered along the search. Mosaic is assessed on the OpenML 100 benchmark and the Scikit-learn portfolio, with statistically significant gains over AutoSkLearn, winner of all former AutoML challenges.

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