The Genetic Algorithm is an evolutionary algorithm inspired by Charles Darwin’s theory of natural selection. It may be one of the most popular biologically inspired algorithms. In this talk, you will discover how a genetic optimization algorithm works and how to implement it from scratch in Python.

## Abstract

Genetic Algorithm (GA) is a stochastic global search optimization algorithm inspired by the biological theory of evolution. It is commonly used to find solutions to search and optimization problems.

In this talk I will explain how GAs work going through a Python example implemented from scratch. I will show how the genetic algorithm, starting from a population of candidate solutions repeatedly evolves them toward better solutions trying to solve an optimization problem. I will illustrate a Python implementation of the main biologically inspired operators of GAs such as selection, crossover and mutation. Finally, I will mention limitations and variants of genetic algorithms.

After the talk, you will be able to implement your own genetic algorithm and apply it to solve a large variety of optimization problems, especially those that are not well suited for standard optimization techniques, like problems in which the objective function is non-differentiable or highly non-linear.