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Release Notes

v1.3.0 - py 0.0.14

Radiate has reached 1.3.0! This release includes a major refactor of the engine's iteration model, a new expression DSL pass and operators, a substantial NSGA III simplification and optimization, a NEAT implementation refactor, and various other improvements and cleanups across the codebase. The engine is now much more efficient, and the new expression DSL features should make it easier to implement complex adaptive behaviors without custom code. The user guide has been rebuilt around testable snippets, and a new guide on diversity and speciation has been added.

Check the changelog for a full list of changes.

v1.2.22 - py 0.0.13

Breaking changes:

  • Changed the checkpointing feature in python to use .pkl as a default extension instead of .json. It fits better within the python ecosystem.
  • Changed the metric names to use . as a separator instead of _. This allows for better organization and grouping of metrics. For example, scores.best instead of best_scores.

Other

Speed improvements centered around engine steps.

Additions

Added a new crate radiate-expr which includes expressions (think polars) to extend the metric and rating systems. This greatly improves the flexibility of dynamic rates (mutation/crossover/species thresholds) and allows users to define their own rating systems. Along with the rate improvements, this extends into the engine itself by allowing users to define their own metrics and use them in the aforementioned dynamic rates - or simply just to track the engine.

Refactored radiate-ui to give much more insight into the engine and the metrics it produces. Included a new search bar and species panel to quickly find and visualize specific species and their members.

In python, radiate now supports optional features (check the user guide installation section for specific info). This lets users opt in to specific integrations within the python ecosystem (e.g. pandas, polars, matplotlib, torch, numpy) without needing to install a bunch of dependencies they may not need.

For checkpointing, new traits were added: CheckpointWriter & CheckpointReader to let users define their own ways of saving checkpoints.

Full Changelog

v1.2.21 - py 0.0.11

Breaking changes:

Changed the FloatGene<T> to take a generic parameter to take either an f32 or f64 value.

  • The above is a breaking change. Just add the generic type to your FloatGene, FloatChromosome, or FloatCodec if needed.

Massive expansion of python's api - check the docs for usage. We moved towards a builder pattern for the engine and added better type hinting.

Full Changelog

v1.2.20 - py 0.0.10

Adding radiate-ui crate, bug fixes, & speed improvements.

I split up some functionality into a new crate radiate-utils and have added a new feature radiate-ui for a tui user interface through ratitui. Some small bug fixes, code simplifications, and some nice little speed improvements.

Full Changelog

v1.2.19 - py 0.0.9

Adding support for experimental PGM or Probabilistic graphical models through the GP feature (crate).

Note

PGM support was experimental and has since been removed; it is no longer part of Radiate.

Major cleanup or unused code and massive graph performance improvements through the use of smallvec as connections instead of BTreeSets.

Improving eventing system through cleaner code and removing redundant events.

Introducing radiate-error (RadiateError) into the core crates ad requiring its usage in certain traits (Problem mainly). We also use this error type in py-radiate and allow it to bubble up into python's type system too.

Brining metrics to the forefront in python.

Full Changelog

v1.2.18 - py 0.0.8

Fixing subtle bug in recurrent graphs where a random seed wasn't being respected, leading to non-deterministic behavior in some cases. This fix ensures that all random operations within recurrent graphs are consistent and reproducible when a seed is provided.

Added three new types of graphs:

  • LSTM (Long Short-Term Memory) Graphs: These are a type of recurrent neural network (RNN) that can learn long-term dependencies.
  • GRU (Gated Recurrent Unit) Graphs: Similar to LSTMs, GRUs are a type of RNN that are simpler and often more efficient.
  • Mesh Graphs: Graphs structured in a mesh topology.

v1.2.17 - py 0.0.7

In response to github issue #23.

Ensuring that FloatGenes/IntGene's respect their bounds during mutation and crossover. This was a bug where mutated or crossovered genes could exceed their defined bounds, which could lead to invalid individuals in the population. This fix ensures that all FloatGenes/IntGene's remain within their specified bounds after any genetic operation. Also some optimizations and code cleanup for py-radiate. Large additions to tests.

Also adding new mutator: JitterMutator for FloatGenes. This mutator adds a small random value (jitter) to each gene, controlled by a magnitude parameter.

v1.2.16

In response to github issue #22.

Adding support for batch fitness functions and batch engine problems through a new trait (BatchFitnessFn). Some small cleanup on other fitness functions and some chromosome operators.

v1.2.15 - py 0.0.6

Adding Novelty Search to python and refactoring engine building across the rust/python bridge. Improving python's speed. Adding type checking to python and upgrading python package to >= python 3.12 to support new python generics. Improving docs to reference new functionality.

New alters:

  • EdgeRecombinationCrossover for PermutationGenes
  • PolynomialMutator for chromosomes with FloatGenes

Added code path in alters for dynamic mutation/crossover rates. This is in early dev, but an be seen in PolynomialMutator.

v1.2.14 - py 0.0.4

Added support for novelty search, fitness-based novelty, and combined novelty and fitness search. Improved documentation and examples. Improved traits for Engine and introduced one for FitnessFn. Bug fixes for pareto fronts and engine iterators.