This a Go implementation of NeuralEvolution of Augmenting Topologies (NEAT). From the NEAT F.A.Q.
NEAT stands for NeuroEvolution of Augmenting Topologies. It is a method for evolving artificial neural networks with a genetic algorithm. NEAT implements the idea that it is most effective to start evolution with small, simple networks and allow them to become increasingly complex over generations. That way, just as organisms in nature increased in complexity since the first cell, so do neural networks in NEAT. This process of continual elaboration allows finding highly sophisticated and complex neural networks.
More information will be provided on the blog redq.me.
go get github.com/rqme/neatThe API documentation can be found at GoDoc.
The Context and Experiment are the central components of the library. The latter encapsulates everything needed for execution and the former provides access to all the necessary helpers. There are several convenience functions in the starter package.
RedQ.NEAT includes several demonstration experiments, each built at the onset of adding a new feature (like phased mutation) or concept (like Novelty Search). These proof-of-concepts are intended to valid this library with the idea being tested as well as compare different helpers (such as HyperNEAT vs regular NEAT). The experiments are each in their own package in the x/experiments directory.
Each experiment builds off the trials package which provides a way to compare multiple runs of an experiment against each other. This package provides several command line arguments that are common to all experiments and displays its output in the console window. For example, here is the output of the XOR experiment:
$ xor --check-stop --trials 40
Run Iters. Seconds Nodes Conns Fitness Fail Comment
--- --------- --------- --------- --------- --------- ------ ---------
0 28 1.339 9 16 16.000
1 26 1.192 8 14 15.443
2 59 3.384 7 17 16.000
3 59 3.609 14 28 16.000
...
36 45 2.513 11 20 16.000
37 30 1.265 7 12 12.250
38 28 1.246 9 17 16.000
39 19 0.822 6 12 13.930
Summary for trials excluding failures (and time for skipped)
Iters. Seconds Nodes Conns Fitness
--- --------- --------- --------- --------- ---------
AVG 34 1.769 9 17 14.894
MED 32 1.625 9 16 15.996
SDV 13 0.905 2 5 1.637
MIN 9 0.303 5 7 10.782
MAX 66 4.503 15 33 16.000 | flag | description | default |
|---|---|---|
| config-name | Common name used as a prefix to archive files | defaults to the name of the executable |
| config-path | Directory containing the initial configuration file and, if available, state files | Current directory |
| trials | The number of trial runs to perform | 10 |
| check-stop | Experiments which do not end with an explicit stop are considered to have failed. | false |
| show-work | Informs the Evaluator (if it implements Demonstrable) to show its work during evaluation. This is used only for the best genome. | false |
| skip-evolve | Skips evolution and only performs summary of archived runs. Best used with --show-work and setting the config-path to the ArchivePath used in the settings file. | false |
Exclusive OR, or XOR for short, is the starter experiment to verify the NEAT (called Classic in RedQ.NEAT) functionality. Located in the x/examples/xor directory, the package produces a standalone executable file. A configuration file, xor-config.json, is provided.
The experiment provides no new command-line arguments but it is recommended to use --check-stop when running to catch trials that do not produce a solution.
RedQ.NEAT was able to find a solution in 40 out of 40 trials. The median number of nodes and connections were 9 and 16 respectively. The results of this experiment are detailed in the wiki.
The core of this library, often called Classic in the code, was written from the ground up using Dr. Kenneth Stanley's PhD dissertation as a guide. NEAT has changed a bit since that paper and I have made some adjustments based on the F.A.Q. I have also add some flexibility in the design to allow for growing the library via helpers which will provide for adding HyperNEAT, Novelty Search, etc. to the library without changing the core API.
The library and proof-of-concept experiments utilize SVG to visualize the network of the best genome as well as the experiment's history. This visualization is based on the NeuroEvolution Visualization Toolkit (NEVT). Each image is output into an .html file for viewing from your desktop or presented through a web server.