Feedforward Algorithm in NEAT (Neural Evolution of Augmenting Topologies)

Question

I don’t understand how the NEAT algorithm takes inputs and then outputs numbers based on the connection genes, I am familiar with using matrixes in fixed topology neural networks to feedforward inputs, however as each node in NEAT has its own number of connections and isn’t necessarily connected to every other node, I don’t understand, and after much searching I can’t find an answer on how NEAT produces outputs based on the inputs.

Could someone explain how it works?

Answer 1

when I was dealing with this I researched into loop detection using matrix methods etc. https://en.wikipedia.org/wiki/Adjacency_matrix#Matrix_powers

But I found the best way to feedforward inputs and get outputs was with loop detection using a timeout propagation delay at each node:

a feedforward implementation is simple and I started from there:

wait until all incoming connections to a node have a signal then sum-squash activate and send to all output connections of that node. Start from input nodes that already have a signal from the input vector. Manually 'shunt' output nodes with a sum-squash operation once there are no more nodes to be processed to get the output vector.

for circularity (traditional NEAT implementation) I did the same as feedforward with one more feature:

calculate the 'maximum possible loop size' of the network. an easy way to calculate this is ~2*(total number of nodes). No walk from input to any node in the network is larger than this without cycling, therefore the node MUST propagate in this many time steps unless it is part of a cycle. Then I wait until all input connection signals arrive at a node OR timeout occurs (signal has not arrived at a connection within maximum loop size steps). If timeout occurs label the input connections that don't have signals as recurrent. Once a connection is labelled recurrent, restart all timers on all nodes (to prevent a node later in the detected cycle from being labelled recurrent due to propagation latency)

Now forward propagation is the same as feed forward network except: don't wait for connections that are recurrent, sum-squash as soon as all non-recurrent connections have arrived (0 for recurrent connections that don't have a signal yet). This ensures that the first node reached in a cycle is set to recurrent, making it deterministic for any given topology and recurrent connections pass data to the next propagation time step.

This has some first time overhead but is concise and produces the same results with a given topology each time its ran. Note that this only works when all nodes have a path to output so you cant necessarily disable split connections (connections that were made from node addition operations) and prune randomly during evolution without making considerations.

(P.S. This also creates a traditional residual-recurrent network that in theory could be implemented as matrix operations trivially. If I had large networks I would first 'express' by running forward propagation once to get recurrent connections then create a 'tensor per layer' representation for matrix-multiplication operations using recurrent, weight, and signal connection attributes with recurrent connection attribute as a sparse binary mask. I actually started writing a Tensorflow implementation that performed all mutation/augmentation operations with tf.sparse_matrix operations and didn't use any tree objects but I had to use dense operations and the n^2 space consumed is too much for what I need but this allowed the use of the aforementioned adjacency matrix powers trick since in matrix form! At least one other person on Github has done tf NEAT but I'm unsure of their implementation. Also I found this interesting https://neat-python.readthedocs.io/en/latest/neat_overview.html)

Happy Hacking!

Answer 2

That was also a question I struggled while implementing my own version of the algorithm.

You can find the answer in the NEAT Users Page: https://www.cs.ucf.edu/~kstanley/neat.html where the author says:

How are networks with arbitrary topologies activated?

The activation function, bool Network::activate(), gives the specifics. The implementation is of course considerably different than for a simple layered feedforward network. Each node adds up the activation from all incoming nodes from the previous timestep. (The function also handles a special "time delayed" connection, but that is not used by the current version of NEAT in any experiments that we have published.) Another way to understand it is to realize that activation does not travel all the way from the input layer to the output layer in a single timestep. In a single timestep, activation only travels from one neuron to the next. So it takes several timesteps for activation to get from the inputs to the outputs. If you think about it, this is the way it works in a real brain, where it takes time for a signal hitting your eyes to get to the cortex because it travels over several neural connections.

So, if one of the evolved networks is not feedforward, the outputs of the network will change in different timesteps and this is particularly useful in continuous control problems, where the environment is not static, but also problematic in classification problems. The author also answers:

How do I ensure that a network stabilizes before taking its output(s) for a classification problem?

The cheap and dirty way to do this is just to activate n times in a row where n>1, and hope there are not too many loops or long pathways of hidden nodes.

The proper (and quite nice) way to do it is to check every hidden node and output node from one timestep to the next, and see if nothing has changed, or at least not changed within some delta. Once this criterion is met, the output must be stable.

Note that output may not always stabilize in some cases. Also, for continuous control problems, do not check for stabilization as the network never "settles" but rather continuously reacts to a changing environment. Generally, stabilization is used in classification problems, or in board games.