What is AccNEAT?

AccNEAT (Accelerated NEAT) is a fork of Kenneth Stanley's NEAT project. It is primarily concerned with reducing the amount of time required by NEAT to evolve solutions to difficult problems. It is typically able to reduce the time required by at least an order of magnitude, and for difficult problems as much as three orders of magnitude. This acceleration of NEAT is accomplished via the following strategies:

• Take advantage of parallel hardware (i.e. multicore CPUs and GPUs).

• Additional genetic operators (e.g. node delete mutation) and search strategies (e.g. a phased search technique similar to that described by Colin Green ) that serve to prevent explosive growth in complexity of evolved networks.

• Optimizations that improve the original implementation of NEAT without altering its genetic/evolutionary algorithm (e.g. using datatstructures that reduce CPU cache misses and search algorithms that require O(log N) instead of O(N)).

Performance gains are most dramatic for very difficult problems, but benefits can also be seen for small, simple experiments, like evolving a network that can solve XOR. The following table shows the amount of time required to conduct 100 experiments in which a solution for XOR is successfully found, where a different random starting population is used for each experiment:

Table 1: Time required to pass 100 XOR experiments

Configuration	Time
NEAT	295 seconds
AccNEAT, 1 CPU core	66 seconds
AccNEAT, 2 CPU cores	39 seconds
AccNEAT, 4 CPU cores	24 seconds
AccNEAT, 8 CPU cores	16 seconds
AccNEAT, 12 CPU cores	13 seconds
AccNEAT, GPU	13 seconds

Note: All configurations used the "complexify" search algorithm and a population size of 10,000. The CPU was an Intel Xeon X5660 running at 2.8 GHz, and the operating system was CentOS 6.5.

The seq-1bit-4el experiment (provided with AccNEAT) is considerably more difficult than XOR, making it a better showcase for AccNEAT's improved search algorithm and parallelism. Table 2 shows how many generations were processed over 10 minute intervals using the complexify and phased search algorithms, where complexify is the search algorithm used in the original NEAT implementations and phased is inspired by the algorithm used in SharpNEAT. The first number is the cumulative number of generations processed through that time, while the number in parentheses shows how many generations were processed in that interval.

Table 2: Generations processed

Time	CPU complexify	GPU complexify	CPU phased	GPU phased
10 minutes	632	848	1,006	2,151
20 minutes	904 (+272)	1,235 (+387)	1,667 (+661)	3,809 (+1658)
30 minutes	1,123 (+219)	1,532 (+297)	2,183 (+516)	4,969 (+1160)
40 minutes	1,307 (+184)	1,779 (+247)	2,714 (+531)	5,992 (+1023)
50 minutes	1,469 (+162)	1,993 (+214)	3,203 (+489)	6,940 (+948)
60 minutes	1,616 (+147)	2,182 (+189)	3,695 (+492)	7,886 (+947)

Note: CPU configurations used 12 cores

One important point to take from this table is that, unlike XOR, the use of a GPU provides significant gains over 12 CPU cores. In general, the larger the networks being executed, the more benefit will be gained from parallel hardware. Perhaps the more important thing to note is that the complexify experiments show a consistent trend of processing fewer generations in every subsequent time interval. This trend consistently holds, and complexify runs will effectively asymptote and fail to make any more progress.

Table 3 shows the fitness scores of the experiments shown in Table 2.

Table 3 Fitness *(1.0 = perfect)

Time	CPU complexify	GPU complexify	CPU phased	GPU phased
10 minutes	0.876120	0.881474	0.890474	0.884226
20 minutes	0.876120	0.883114	0.898549	0.925414
30 minutes	0.878473	0.883114	0.919856	0.932065
40 minutes	0.878473	0.883114	0.919856	0.941101
50 minutes	0.878473	0.883114	0.919856	0.941101
60 minutes	0.878473	0.895156	0.933002	0.941101

The complexify runs will never go on to achieve a score much above a 0.90; their progress will grind to a halt as their genomes become too big. The phased runs, however, will achieve a 1.0 fitness. While not shown in Table 3, the GPU phased experiment went on to achieve a 1.0 fitness at generation 9,837, which took 86 minutes.

What is the status of AccNEAT?

As of October 2014, AccNEAT is under active development.

If you want to use AccNEAT, you will need to download the source code and create your experiments within the source tree. The structure of the project will hopefully be updated in the near future such that AccNEAT is a library, allowing users to conveniently develop their own experiments outside the AccNEAT source tree.

System Requirements

• AccNEAT is currently only used on Linux (Xubuntu 14.04 and CentOS 6.5). It shouldn't be too painful to run on other POSIX systems, but the build system is not designed ideally for portability.

• C++ compiler with full support for C++11 standard (e.g. GCC 4.9)

• NVCC and an Nvidia graphics card if you want to use a GPU accelerator. The Cuda code is currently written to support 1.3 compute capability devices. The only NVCC version that has been tried is 6.0, but an earlier version may work as well. Note that NVCC won't work with GCC 4.9, so you'll need to have an older version of GCC as well! GCC 4.1 and 4.4 have worked fine.

Installing/Building

Download the source:

git clone https://github.com/sean-dougherty/accneat.git

Configure:

cd accneat
./configure

This is not a proper configure script. It just makes a default Makefile.conf, which is in turn included by Makefile. You can modify the contents of Makefile.conf to enable GPU support (set ENABLE_CUDA=true) or to enable the debug build (DEVMODE=true). You may also use it for platform-specific settings. See Makefile.xubuntu and Makefile.maxwell, which are versions of Makefile.conf that I use on my Xubuntu laptop and on a CentOS cluster.

Build:

make

Running experiments

Experiments are executed via the ./neat command. Executing with no arguments will provide a usage message:

usage: neat [OPTIONS]... experiment_name

experiment names: cfg-XSX, foo, lessthan, regex-XYXY, regex-aba, regex-aba-2bit, seq-1bit-2el, seq-1bit-3el, seq-1bit-4el, xor

OPTIONS
  -f                   Force deletion of any data from previous run.
  -c num_experiments   (default=1)
  -r RNG_seed          (default=1)
  -n population_size   (default=1000)
  -x max_generations   (default=10000)
  -s search_type       {phased, blended, complexify} (default=phased)

So, to run the XOR experiment 10 times with a population size of 5,000, and using the complexify search, you would type:

./neat -c 10 -n 5000 -s complexify xor

Results will be written to directories named ./experiment_i. Note that neat will refuse to run if ./experiment_* directories already exist, unless the -f option is specified, which will delete the old directories.

Making your own experiments

For an example of how to make your own experiment, look at src/experiments/static/xor.cpp, which shows a simple declaration of input and output. For an example of a more complicated setup in which the inputs/outputs are programatically generated, see src/experiments/static/regex.cpp. For an example of an embodied experiment, see src/experiments/maze. Simply put your source file under the src/experiments directory and it should be automatically built and will be available from the command-line tool.