LEAP: Evolutionary Algorithms in Python

Written by Dr. Jeffrey K. Bassett, Dr. Mark Coletti, and Dr. Eric Scott

LEAP is a general purpose Evolutionary Computation package that combines readable and easy-to-use syntax for search and optimization algorithms with powerful distribution and visualization features.

LEAP's signature is its operator pipeline, which uses a simple list of functional operators to concisely express a metaheuristic algorithm's configuration as high-level code. Adding metrics, visualization, or special features (like distribution, coevolution, or island migrations) is often as simple as adding operators into the pipeline.

Using LEAP

Get the stable version of LEAP from the Python package index with

pip install leap_ec

Simple Example

The easiest way to use an evolutionary algorithm in LEAP is to use the leap_ec.simple package, which contains simple interfaces for pre-built algorithms:

from leap_ec.simple import ea_solve

def f(x):
    """A real-valued function to optimized."""
    return sum(x)**2

ea_solve(f, bounds=[(-5.12, 5.12) for _ in range(5)], maximize=True)

Genetic Algorithm Example

The next-easiest way to use LEAP is to configure a custom algorithm via one of the metaheuristic functions in the leap_ec.algorithms package. These interfaces offer you a flexible way to customize the various operators, representations, and other components that go into a modern evolutionary algorithm.

Metaheuristics are usually defined by three main objects: a Problem, a Representation, and a pipeline (list) of Operators.

Here's an example that applies a genetic algorithm variant to solve the MaxOnes optimization problem. It uses bitflip mutation, uniform crossover, and binary tournament_selection selection:

Python code for simple GA

```Python from leap_ec.algorithm import generational_ea from leap_ec import ops, decoder, probe, representation from leap_ec.binary_rep import initializers from leap_ec.binary_rep import problems from leap_ec.binary_rep.ops import mutate_bitflip pop_size = 5 final_pop = generational_ea(max_generations=10, pop_size=pop_size, # Solve a MaxOnes Boolean optimization problem problem=problems.MaxOnes(), representation=representation.Representation( # Genotype and phenotype are the same for this task decoder=decoder.IdentityDecoder(), # Initial genomes are random binary sequences initialize=initializers.create_binary_sequence(length=10) ), # The operator pipeline pipeline=[ # Select parents via tournament_selection selection ops.tournament_selection, ops.clone, # Copy them (just to be safe) # Basic mutation with a 1/L mutation rate mutate_bitflip(expected_num_mutations=1), # Crossover with a 40% chance of swapping each gene ops.uniform_crossover(p_swap=0.4), ops.evaluate, # Evaluate fitness # Collect offspring into a new population ops.pool(size=pop_size), probe.BestSoFarProbe() # Print the BSF ]) ```

Low-level Example

However, it may sometimes be necessary to have access to low-level details of an EA implementation, in which case the programmer can arbitrarily connect individual components of the EA workflow for maximum tailorability. For example:

Low-level example python code

```python from toolz import pipe from leap_ec.individual import Individual from leap_ec.decoder import IdentityDecoder from leap_ec.context import context import leap_ec.ops as ops from leap_ec.binary_rep.problems import MaxOnes from leap_ec.binary_rep.initializers import create_binary_sequence from leap_ec.binary_rep.ops import mutate_bitflip from leap_ec import util # create initial rand population of 5 individuals parents = Individual.create_population(5, initialize=create_binary_sequence(4), decoder=IdentityDecoder(), problem=MaxOnes()) # Evaluate initial population parents = Individual.evaluate_population(parents) # print initial, random population util.print_population(parents, generation=0) # generation_counter is an optional convenience for generation tracking generation_counter = util.inc_generation(context=context) while generation_counter.generation() < 6: offspring = pipe(parents, ops.tournament_selection, ops.clone, mutate_bitflip(expected_num_mutations=1), ops.uniform_crossover(p_swap=0.2), ops.evaluate, ops.pool(size=len(parents))) # accumulate offspring parents = offspring generation_counter() # increment to the next generation util.print_population(parents, context['leap']['generation']) ```

More Examples

A number of LEAP demo applications are found in the the example/ directory of the github repository:

git clone https://github.com/AureumChaos/LEAP.git
python LEAP/examples/advanced/island_models.py

Demo of LEAP running a 3-population island model on a real-valued optimization problem.

Documentation

The stable version of LEAP's full documentation is over at ReadTheDocs.

If you want to build a fresh set of docs for yourself, you can do so after running make setup:

make doc

This will create HTML documentation in the docs/build/html/ directory. It might take a while the first time, since building the docs involves generating some plots and executing some example algorithms.

Installing from Source

To install a source distribution of LEAP, clone the repo:

git clone https://github.com/AureumChaos/LEAP.git

And use the Makefile to install the package:

make setup

Run the Test Suite

LEAP ships with a two-part pytest harness, divided into fast and slow tests. You can run them with

make test-fast

and

make test-slow

respectively.

pytest output example

AureumChaos / LEAP

readme