NTBEA

An implementation of the N-Tuple Bandit Evolutionary Algorithm

@inproceedings{NTBEA-Game-Tuning,
  title={The N-Tuple Bandit Evolutionary Algorithm for Automatic Game Improvement},
  author={Kunanusont, Kamolwan and Gaina, Raluca D. and Liu, Jialin and Perez-Liebana, Diego and Lucas, Simon M.},
  booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)},
  note{\url{https://arxiv.org/pdf/1705.01080.pdf}},
  year={2017}
}

Installation

using pip

pip install ntbea

Usage

To use the NTBEA algorithm, you will need to define the following:

Search Space

The search space defines the potential parameters in their respective dimensions

Evaluator

The evaluator scores is used to score the combination of parameters for the optimiztion problem

NTupleLandscape

The NTuple landscape is the set of tuples which are used to choose combinations of parameters to test and score

Examples

Examples of setting up the Search Space, Evaluator and NTupleLandscape can be found in the examples directory and run with:

python run.py

m_max example

max_dims = 6
max_m = 4

# Set up the problem domain as one-max problem
search_space = MMaxSearchSpace(max_dims, max_m)
evaluator = MMaxEvaluator()

# 1-tuple, 2-tuple, 3-tuple and N-tuple
tuple_landscape = NTupleLandscape(search_space, [1,2,max_dims])

# Set the mutator type
mutator = DefaultMutator(search_space, mutation_point_probability=0.5)

evolutionary_algorithm = NTupleEvolutionaryAlgorithm(tuple_landscape, evaluator, search_space, mutator,
                                                     k_explore=2.0, eval_neighbours=50)

evolutionary_algorithm.run(5000)

Cite

If you want to cite this library, please use the following DOI