gevopy 1.1.1

Genetics for Evolutionary Algorithms in Python.

Awesome Genetics for Evolutionary Algorithms library created by Borja Esteban.

Install it from PyPI

$ pip install gevopy

Usage

This package is designed in order to create your own evolution scripts based on the following concepts:

• Chromosomes: Genetic instructions for phenotypes.
• Genotype: Genetic design to instantiate phenotypes.
• Phenotypes: Genotype instances which perform a task.
• Fitness: Provide the methods to evaluate phenotypes.
• Algorithm: Evolution procedure for phenotypes.
• Experiment: Evolution session with phenotypes.

Now the following sections will introduce a fast initialization to the package. Do not hesitate to extend your knowledge by using all the additional provided examples at the folder examples.

Genotypes

Define your Genotypes following the dataclass principles from pydantic by using the base model GenotypeModel. All dataclass attributes are accepted in addition to an special type Chromosome provided in the module genetics. To start use the already defined chromosome subclasses such Haploid and Diploid depending on the complexity of your genetic model.

from gevopy import genetics, random
from gevopy.genetics import Field

class MyGenotype(genetics.GenotypeModel):
    chromosome_1: genetics.Haploid = Field(default_factory=lambda: random.haploid(12))
    chromosome_2: genetics.Haploid = Field(default_factory=lambda: random.haploid(10))
    simple_attribute: float = 1.0

[MyGenotype() for _ in range(2)]

[{'id': UUID('8b77fc1d-befe-4ad3-924c-1774223b7b60'),
  'experiment': None,
  'created': datetime.datetime(2023, 3, 4, 15, 24, 49, 325435),
  'parents': [],
  'generation': 1,
  'score': None,
  'chromosome_1': Haploid([0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0], dtype=uint8),
  'chromosome_2': Haploid([1, 0, 1, 1, 1, 0, 0, 1, 0, 1], dtype=uint8),
  'simple_attribute': 1.0},
 {'id': UUID('a4460974-a45a-4ed2-8937-55ea211bb520'),
  'experiment': None,
  'created': datetime.datetime(2023, 3, 4, 15, 24, 49, 325564),
  'parents': [],
  'generation': 1,
  'score': None,
  'chromosome_1': Haploid([1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0], dtype=uint8),
  'chromosome_2': Haploid([1, 0, 1, 1, 1, 0, 0, 1, 0, 1], dtype=uint8),
  'simple_attribute': 1.0}]

Note Genotype attrubutes id, experiment, created, parents, generation, score and clone are attributes used by the library. Overwriting of this attributes might lead to unexpected behaviors.

Fitness

Create your fitness using the parent class fitness.FitnessModel and defining the class method score. The fitness to use on the experiment will be an instance of the defined class. You can use the init arguments cache and scheduler (from Dask) to optimize how the evaluation flow is executed.

from gevopy import fitness

class MyFitness(fitness.FitnessModel):
    def score(self, phenotype):
        x1 = phenotype.chromosome_1.count(1)
        x2 = phenotype.chromosome_2.count(0)
        return x1 - x2

MyFitness(cache=True, scheduler="threads")

<__main__.MyFitness at 0x7f19e0744f40>

You can additionally define setup as method to execute once at the begining of each generation before phenotypes are evaluated.

The only accepted values for scheduler are synchronous, threads and processes. By default threads is used.

Algorithm

The algorithm is the core of your experiment. It defines the rules of the evolution process. You can create your own algorithm or use the already existing templates. Algorithms are generally composed by 4 components:

• Selection: Callable which provides the first list of candidates.
• Mating: Callable which provides the second list of candidates.
• Crossover: Callable to generate offspring from candidates.
• Mutation: Callable to mutate phenotype's chromosomes.

Additionally, each algorithm template might contain additional arguments such a survival_rate or similarity. Make sure you read and understand each of the arguments and steps.

from gevopy.tools import crossover, mutation, selection
from gevopy import algorithms

class MyAlgorithm(algorithms.Standard):
    selection1 = selection.Tournaments(tournsize=3)
    selection2 = selection.Uniform()
    crossover = crossover.Uniform(indpb=0.01)
    mutation = mutation.SinglePoint(mutpb=0.2)

MyAlgorithm()

MyAlgorithm(selection1=<gevopy.tools.selection.Tournaments object at 0x7f19906ca680>, mutation=<gevopy.tools.mutation.SinglePoint object at 0x7f19906ca710>, selection2=<gevopy.tools.selection.Uniform object at 0x7f19906ca770>, crossover=<gevopy.tools.crossover.Uniform object at 0x7f19906c8ee0>, survival_rate=0.4)

The modules tools.crossover, tools.mutation and tools.selection contain templates and utilities to simplify your algorithm definition.

Experiment

The experiment is the final expression of your evolutionary algorithm. it provides the methods to evolve and store phenotypes. Once an experiment is instantiated, use the method run to force the evolution of the population until a desired state.

The results of the experiment can be collected from the method output, calling best method or adding a Neo4j connection as database input when instantiating the experiment to store all phenotypes during the execution.

import gevopy as ea

experiment = ea.Experiment()
with experiment.session() as session:
    session.add_phenotypes([MyGenotype() for _ in range(20)])
    session.algorithm = MyAlgorithm(survival_rate=0.2)
    session.fitness = MyFitness(cache=True, scheduler="synchronous")
    statistics = session.run(max_generation=20, max_score=10)

experiment.close()
statistics

Evolutionary algorithm execution report:
  Executed generations: 12
  Best phenotype: 7b13630f-d07c-4ff6-8be1-df6d6ceb06ca
  Best score: 10

The method run forces the evolution of the experiment which is updated on each cycle. After the method is completed, you can force again te evolution process using higher inputs for max_generations or max_score.

Development

Fork the repository, pick one of the issues at the issues and create a Pull request.

FAQ and Notes

Why Graph Database?

Storing relationships at the record level makes sense in genotype relationships as it provides index-free adjacency. Graph traversal operations such 'genealogy tree' or certain matches can be performed with no index lookups leading to much better performance.

Why pydantic instead of dataclass?

Pydantic supports validation of fields during and after the initialization process and makes parsing easier. Parsing is a relevant step if you are planing to save your phenotypes into the connected database.

Limitations

Collections containing collections can not be stored in properties. Property values can only be of primitive types or arrays in Neo4J Cypher queries.