snes

Separable Natural Evolution Strategy algorithm in JavaScript.

<img src="examples/rects-output.png" width="50%" />

Mona Lisa in 200 rectangles, see demo

Quick Example

import SNES from "snes";

// creates an optimizer with settings
const optimizer = SNES({
  solutionLength, // number of parameters
  populationCount,
});

// an array to store your fitnesses for each population
const fitnesses = new Float32Array(populationCount);

// for each epoch...
const epochs = 100;
for (let i = 0; i < epochs; i++) {
  // ask for a set of solutions (flat array)
  const solutions = optimizer.ask();

  // compute the fitness for each
  for (let j = 0; j < populationCount; j++) {
    // note this returns a subarray (i.e. no copy)
    const params = optimizer.getSolutionAt(solutions, j);

    // compute the fitness, which is often -err
    fitnesses[j] = fitness(params);
  }

  // update the optimizer with all fitnesses
  optimizer.tell(fitnesses);
}

// The optimized 'mean' solution
console.log(optimizer.center);

See the examples directory for more.

Install

Use npm to install.

npm install snes --save

Usage

optimizer = SNES({ opts })

With options:

• solutionLength number of parameters to optimize
• populationCount number of candidate solutions to use
• alpha (default=0.05) learning rate
• state a 4-element Uint32Array random seed state
• random (default=Math.random) randomizer for computing an initial opts.state if not specified; ignored if state is given
• sigma the initial standard deviation, defaults to:
  • new Float32Array(solutionLength).fill(1)
• center the initial mean, defaults to:
  • new Float32Array(solutionLength).fill(0)

solutions = optimizer.ask()

Returns a flat array of solutions, strided by solutionLength. The total size of this array will be solutionLength * populationCount.

subarray = optimizer.getSolutionAt(solutions, index)

From a flat array of solutions, gets a subarray at slot index. Since this is a view of the flat array, it is not a copy, and so any changes to the flat array will also be present in this view. You should use subarray.slice() if you want a copy.

optimizer.tell(fitnesses)

Updates the parameters based on the list of fitnesses, which is expected to be parallel to the solutions array given by the ask() function. The size of this array should be populationCount.

optimizer.center

The current mean of the optimizer, i.e. the optimization result. This may not always be the best performing candidate, for example compared to candidates from prior epochs.

other getters

• optimizer.sigma (length solutionLength)
• optimizer.gaussian (length populationCount * solutionLength)
• optimizer.prng (you can use prng.next() and prng.nextGaussian() for random values)

From Source

Clone the repo, cd into it and npm install dependencies, then you can run:

npm run rects

And open the localhost URL in your browser to see Mona Lisa painted in 200 rectangles.

You can also test with node examples/ascii.js for a simple textual learning example.

References

• Exponential Natural Evolution Strategies (2010) - T Glasmachers
• Benchmarking Separable Natural Evolution Strategies on the Noiseless and Noisy Black-box Optimization Testbeds (2012) - T Schaul
• pints

License

MIT, see LICENSE.md for details.