Disclaimer: this is currently a work in progress.
Multi-population evolutionary optimisation workbench
You can define the algorithm in a single Elixir script file like this:
# Install Meow and Nx for numerical computing
Mix.install([
{:meow, "~> 0.1.0-dev", github: "jonatanklosko/meow"},
{:nx, "~> 0.7.0"},
{:exla, "~> 0.7.0"}
])
Nx.Defn.global_default_options(compiler: EXLA)
# Define the evaluation function, in this case using Nx to work with MeowNx
defmodule Problem do
import Nx.Defn
def size, do: 100
@two_pi 2 * :math.pi()
defn evaluate_rastrigin(genomes) do
sums =
(10 + Nx.pow(genomes, 2) - 10 * Nx.cos(genomes * @two_pi))
|> Nx.sum(axes: [1])
-sums
end
end
# Define the evolutionary algorithm
algorithm =
Meow.objective(
# Specify the evaluation function that we are trying to maximise
&Problem.evaluate_rastrigin/1
)
|> Meow.add_pipeline(
# Define how the population is initialized and what representation to use
MeowNx.Ops.init_real_random_uniform(100, Problem.size(), -5.12, 5.12),
# A single pipeline corresponds to a single population
Meow.pipeline([
# Define a number of evolutionary steps that the population goes through
MeowNx.Ops.selection_tournament(1.0),
MeowNx.Ops.crossover_uniform(0.5),
MeowNx.Ops.mutation_replace_uniform(0.001, -5.12, 5.12),
MeowNx.Ops.log_best_individual(),
Meow.Ops.max_generations(5_000)
])
)
# Execute the above algorithm
report = Meow.run(algorithm)
report |> Meow.Report.format_summary() |> IO.puts()Then you simply run the script
$ elixir example.exs
──── Summary ────
Total time: 5.993s
Populations: 1
Population time (mean): 5.746s
Generations (mean): 5000
──── Best individual ────
Fitness: -9.421020843939452
Generation: 4982
Genome: #Nx.Tensor<
f64[100]
EXLA.Backend<host:0, 0.3115944784.4181327880.13497>
[0.009958496317267418, 0.04785522446036339, 0.017037352547049522, -0.017465820536017418, -0.017824707552790642, 0.008717365553470262, -0.032379150390625, 8.666992071084678e-4, 0.001085205003619194, 0.023798827081918716, -0.0013952635927125812, 0.0050024413503706455, 0.013221435248851776, -0.011859131045639515, -0.07148071378469467, -0.0018774414202198386, -0.005152587778866291, -0.005764159839600325, -0.013275146484375, -0.012554931454360485, 7.470702985301614e-4, 0.024797363206744194, 0.018918546728446277, 0.018818359822034836, 0.03758788853883743, 0.0024987792130559683, -0.01250610314309597, 3.4667967702262104e-4, 0.025520019233226776, -0.014041747897863388, 0.0024865721352398396, -0.00942260678857565, -0.0036364744883030653, 0.001259765587747097, 0.020170897245407104, 0.03543701022863388, 0.01214599609375, -0.008547362871468067, 0.008267821744084358, -0.008248290978372097, 0.009224853478372097, -0.009011230431497097, -0.01357421837747097, -0.0022021483164280653, 0.002288818359375, 0.009760742075741291, -0.01131591759622097, 0.04674072191119194, -0.0051379394717514515, -0.035057373344898224, ...]
>Check out more examples in the examples directory.
To iterate on different versions of your algorithm in a more interactive fashion we recommend using Livebook. For a quick introduction you can import the Rastrigin notebook.
Copyright (C) 2021 Jonatan Kłosko and Mateusz Benecki
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.