Creating genetic algorithm applications is easier than ever before.

A Genetic algorithm (GA) is the offspring of Charles Darwin’s theory of natural evolution. The algorithm is built around the idea of natural selection where individuals in a population reproduce in the hopes of producing better offspring. This process continues for multiple generations, in hopes of producing the desired result.

With python packages, this complex process has been simplified. The EasyGA python package has brought the complexity and time consuming process of writing a proper GA to minutes rather than hours.

Introduction to genetic algorithms — Including Example Code

Let’s start with a beginners example. I was inspired by Vijini Mallawaarachchi’s introductory article when I first started writing natural selection algorithms so for me it is natural to use her problem of getting all 1’s in the gene pool or in this case we will call a gene pool a chromosome.

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