Machine learning is a massive topic, but it is possible to cover some interesting and useful aspects in a short workshop. For example, inspired by the biological process of evolution in nature, evolutionary algorithms have been widely used for optimization problems, i.e. searching for the ‘best’ solution(s) to a problem from a space of possibilities. This workshop is aimed at programmers who wish to better understand a variety of evolutionary algorithms. Examples will be given in both Python and C++.

The workshop begins with a brief introduction on evolutionary algorithms and their history, and how they have taken biological evolution as inspiration. We will start with ‘OneMax’, a ‘hello world’-type example for evolutionary algorithms and work our way to more complicated examples. By the end of the workshop, participants will have gained a practical understanding of the important patterns of evolutionary algorithms (e.g. solution representations, fitness measures, diversity preservation operators).

We will learn how randomness can be used to solve problems.

Target Audience: Developers
Prerequisites: English, able to code in Python or C++, with your tools to hand
Level: Basic