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Using Algorithms

Basic Interface

Once a SearchAlgorithm instance has been created, e.g.

from evotorch import Problem
from evotorch.algorithms import SNES
import torch
def sphere(x: torch.Tensor) -> torch.Tensor:
    return torch.sum(x.pow(2.))
problem = Problem(
    'min',
    sphere,
    solution_length = 10,
    initial_bounds = (-1, 1),
)
searcher = SNES(problem, stdev_init = 5)

the main usage is to step the algorithm forward by a single generation

searcher.step()

However, a common use-case is to run a SearchAlgorithm instance for many generations. In this case, the run function provides an easy interface for this,

searcher.run(num_generations=100)

Important

The above call to run will not produce any output as the searcher has no attached loggers.

Accessing the Status

Each SearchAlgorithm instance maintains a status dictionary which tracks various information about the current status of the searcher. You can discover the available status information for a specific class that inherits SearchAlgorithm using,

print([k for k in searcher.iter_status_keys()])

Each status property can be accessed by its name

best_discovered_solution = searcher.status['best']

All algorithms currently implemented in EvoTorch applied to single-objective problems will at least have the following status properties:

  • 'best', the best discovered solution so far.
  • 'worst', the worst discovered solution so far.
  • 'best_eval', the fitness of the best discovered solution so far.
  • 'worst_eval', the fitness of the worst discovered solution so far.
  • 'pop_best', the best solution in the population.
  • 'pop_best_eval', the fitness of the best solution in the population.
  • 'mean_eval', the mean fitness of the population.
  • 'median_eval', the best solution in the population.

Changing Data-types and Devices

In EvoTorch, problems can be specified to use a specific torch device and data type. When a SearchAlgorithm instance is created, it is passed a Problem instance, and in doing so, inherits the device and data type from the Problem.

This is easy to observe, as running the following code,

prob = Problem(
    'min',
    sphere,
    solution_length = 10,
    initial_bounds = (-1, 1),
    dtype = torch.float16,
    device = 'cuda:0',
)
searcher = SNES(prob, stdev_init = 5)
searcher.run(10)

and then printing the center of the search distribution using the status dictionary, will show that the resulting tensor is now using the dtype and device that was specified in the instantiation of prob

print(searcher.status['center'])
Output 
ReadOnlyTensor(
    tensor([-0.5435,  1.5527, -2.0664,  1.2012,  0.2749, -1.7686, -0.4634,  5.5039,
            -1.4092, -0.7285, -0.3555, -1.5322, -0.9805, -1.7363, -4.3633, -3.1953,
            -3.3008,  0.5483,  0.3359,  0.0964,  2.3184, -2.7031,  1.4873,  0.2109,
            1.4775], device='cuda:0', dtype=torch.float16)
)