The routine includes binary coding for the individuals, jump mutation, creep mutation, and the option for single-point or uniform crossover. More recently, an option for the use of a micro-GA has been added. Population level operators include steady state, roulette wheel and tournament selection. Easy sample file loading and configuration file parsing. The documentation includes an extensive overview of how to implement a genetic algorithm as well as examples illustrating customizations to the GAlib classes.
GAS is freeware.
Purpose of GAS is to explore and exploit artificial evolutions. Primary implementation language of GAS is Python. The GAS software package is meant to be a Python framework for applying genetic algorithms. It contains an example application where it is tried to breed Python program strings. Nevertheless, GAS tries to be useful for other applications of Genetic Algorithms as well.