TFNE Development

Version History

v0.21.1 (3rd Sep, 2020)

  • Fix bug of missing TFNEV icons in PyPI package

  • Fix bug of logging level in examples being interpreted as string instead of int

v0.21.0 (3rd Sep, 2020)

  • Bug fix and performance optimize beta version of CoDeepNEAT, releasing CoDeepNEAT fully with all intended speciation methods

  • Implement TFNE Visualizer with capability to process CoDeepNEAT population backups

  • Create ReadTheDocs documentation for TFNE

  • Implement extensive unit tests and coverage reporting

  • Make TFNE available via PyPI

v0.2 (5th June, 2020)

  • Extensively rework framework architecture to enable higher modularity, performance and maintainability

  • Release beta version of CoDeepNEAT, including basic speciation

  • Implement MNIST and CIFAR10 environments with corresponding examples

  • Implement full periodic state backup and deserialization

v0.1 (23rd March, 2020)

  • Initial release providing prototyping framework for neuroevolution with TF 2.x

  • Implement NEAT algorithm with direct encoding and fully TF translateable phenotype

  • Implement OpenAI gym test environments and provide examples in combination with NEAT

  • Implement configurable reporting agents that backup the population and visualize genomes each generation

Future ToDos

General

  • Implement custom tf.keras.preprocessing functions as used for example in CDN paper, using tf.image API/functions. Integrate those preprocessing options into the TFNE environments.

  • Implement a test-wise multi-objective evolution environment [e.g. decreasing fitness based on age; decreasing fitness based on genome complexity]

Code Improvements

  • Implement bidirection connections for the phenotype graphs of all NEAT-like algorithms, as they currently only support feedforward graphs.

  • Replace numpy with Google JAX backend and test if it improves performance. Especially relevant for vanilla NEAT, which utilizes numpy extensively.

  • Attempt to accelerate TFNE and its algorithms (particularly vanilla NEAT)using tf.function

NEAT

  • Completely rework NEAT implementation. Accelerate implementation by parallelizing it. Test possible acceleration by using Google JAX.

  • Implement flag to either disable bias, to allow bias being a separate node or to integrate bias into nodes

DeepNEAT

  • Implement DeepNEAT. Consider layers: Attention, AveragePoolingX, BatchNormalization, BiDirectional, ConvX, Dense, Dropout, Embedding, Flatten, GRU, GlobalAvgPoolX, GlobalMaxPoolX, LSTM, MaxPoolingX, SimpleRNN

CoDeepNEAT

  • CoDeepNEAT: Create function to register new modules created outside of code structure. This way it is easier to add new modules if you use TFNE exclusively as a library.

SubGraphNEAT

SubGraphDeepNEAT

Research Ideas

  • Find way to determine if 2 genomes represent identical phenotypes or identical functions but have different genotypes and are therefore classified as different species, therefore decreasing effective pop size.

  • Implement Lamarckian Evolution for weight-training based NE algorithms (DeepNEAT, CoDeepNEAT, etc)

  • Use a gram matrix to cluster member vectors and direct evolution