Genex v1.0.1-beta API Reference

Genex makes it easy to write Evolutionary Algorithms in Elixir. The process of creating an algorithm in Genex can be thought of in three phases

Evolution behaviour definition for Evolutionary algorithms.

Models the most basic form of evolution.

Utilities for saving and loading from checkpoints.

Implementation of a genealogy tree.

Keeps track of the strongest individual of a generation in an ETS table.

Benchmark objective functions for binary genetic algorithms.

Provides benchmark functions for Single Objective Continuous optimization problems.

Provides benchmark functions for Symbolic Regression programs with Genetic Programming.

Implementation of several popular crossover methods.

Evaluation convenience functions.

Convenience functions for Multi-Objective Optimization.

Penalty functions for use with constraint satisfaction problems.

Contains functions for generating various Genotypes.

Implementation of several population mutation methods.

Implementation of several popular selection methods.

Genex representation of a single solution.

A collection of Chromosomes.

Behaviour for implementing visualizations.