Welcome to the GAFT documentation!
GAFT is a general genetic algorithm framwork written in Python with MPI parallelization under the GPLv3 license. It’s an acronym for Genetic Algorithm Framework for Python.
You can always find the latest stable version of the program here: https://github.com/pytlab/gaft
This documentation describes version 0.5.6
Why Genetic Algorithm?¶
In contrast to those local search methods, genetic algorithms which are categorized as global search heuristics are a particular class of evolutionary algorithm (EA) that utilizes techniques inspired by evolutionary biology ideas such as inheritance, mutation, selection and crossover. Without calculating derivatives, genetic algorithms can be used in many domains to find the optimal solutions for complex problems such as biology, engineering, computational chemistry, computer science and social science. Especially in a case where the mathematical data is available and answers are available but the formula that joins the data to the answers is missing, at this time, a genetic algorithm can be used to ‘evolve’ an expression tree to create a very close fit to the data, for example, the complex hyper parameters optimization of a mathematical model. At the same time, genetic algorithms have relative fixed iteration process and large space for algorithm adjustment by genetic operator improvement. Therefore, genetic algorithm is one of the most appropriate methods to construct a general optimization framework for more realistic applications in different fields
Optimization problem that needs is a common problem researchers meet in computational physics and chemistry field. It is a great challenge to create a high-efficient program that are general and easy-to-use enough to be used directly for optimizing different target problems and are customizable enough to help researcher to develop new algorithm and run tests.
To this end, we present a Python general Genetic Algorithm framework named GAFT which provides flexible and customizable API to help researchers in various fields to apply genetic algorithm optimization flow to their own targets.