NEAT
NeuroEvolution of Augmenting Topologies
Description
- The code is written according to my own understanding of the paper.
- The project is still in progress.
Usage
Solving XOR Problem
type python TestXOR.py
usage: TestXOR.py [-h] [--pop POP] [--gen GEN] [--thr THR] [--cmp CMP]
[--mat MAT] [--cpy CPY] [--slf SLF] [--exc EXC] [--dsj DSJ]
[--wgh WGH] [--srv SRV]
Change the evolutionary parameters.
optional arguments:
-h, --help show this help message and exit
--pop POP The initial population size.
--gen GEN The maximum generations.
--thr THR The compatibility threshold.
--cmp CMP The number of genomes used to compare compatibility.
--mat MAT The mating probability.
--cpy CPY The copy mutation probability.
--slf SLF The self mutation probability.
--exc EXC The excess weight.
--dsj DSJ The disjoint weight.
--wgh WGH The average weight differences weight.
--srv SRV The number of survivors per generation.
Finally, the resulting network structure will look like this:
Genome 542(fitness = 4.00):
Total Nodes:5 Hidden Nodes:1
Enabled Connections(7):
[Input Node 1] = 1.00 **[ -4.53]** [Output Node 3] = 0.46 Enable = True Innovation = 0
[Input Node 2] = 1.00 **[ -1.87]** [Output Node 3] = 0.46 Enable = True Innovation = 1
[Bias Node 0] = 1.00 **[ -2.07]** [Output Node 3] = 0.46 Enable = True Innovation = 2
[Bias Node 0] = 1.00 **[ -3.85]** [Hidden Node 4] = 1.00 Enable = True Innovation = 3
[Hidden Node 4] = 1.00 **[ 8.32]** [Output Node 3] = 0.46 Enable = True Innovation = 4
[Input Node 1] = 1.00 **[ 7.42]** [Hidden Node 4] = 1.00 Enable = True Innovation = 5
[Input Node 2] = 1.00 **[ 5.19]** [Hidden Node 4] = 1.00 Enable = True Innovation = 6
Playing Tictactoe game
type python TestTictactoe.py
The AI can learn how to play tictactoe from scratch, but it's very slow to converge.