EvoloPy: An Open-Source Nature-Inspired Optimization Framework in Python

As an initiative to keep an implementation of the recent nature-inspired metaheuristics as well as the classical ones in a single open source framework, we introduce EvoloPy. EvoloPy is an open source and cross-platform Python  framework that implements a wide range of metaheuristic algorithms. The goal is to take the advantage of the rapidly growing scientific community of Python and provide a set of robust optimizers as free and open source software. We believe that implementing such algorithms in Python will increase their popularity and portability among researchers and non-specialists coming from different domains. The powerful libraries and packages available in Python (such as NumPy) will make it more feasible to apply metaheuristics for solving complex problems on a much higher scale.

Our poster of EvoloPy was accepted in the ECTA conference and we recently presented it in Porto. Have a look at the paper and poster source:

Paper source: https://github.com/7ossam81/EvoloPy
Poster source: https://github.com/7ossam81/EvoloPy-poster
List of available optimizers: https://github.com/7ossam81/EvoloPy/wiki/List-of-optimizers

Thank you Python and NumPy :)

Benchmarking evolutionary algorithms

People tend to think that there is a simple way of implementing evolutionary algorithms: whatever language they’re the most familiar with, or, by default, Java or C++. So after receiving several carps from reviewers who didn’t like our use of non-conventional languages like JavaScript of Perl, we decided to test a pile of languages performing simple evolutionary operations: mutation and crossover, and also a very common benchmark, OneMax.
Our poster was accepted in the ECTA conference and we recently presented it in Porto. Have a look at the paper and poster source https://github.com/geneura-papers/2016-ea-languages-PPSN/releases/tag/v1.0ECTA that uses Knitr, and check out the poster.

Evolutionary (and other) algorithms in the cloud

The cloud is where you run your applications, but it’s also how you will design your algorithms from now on. Evolutionary algorithms are specially suited for this, and that is why I have given tutorials on how to adapt evolutionary algorithms to the cloud in PPSN and lately, when one of the keynotes dropped, an updated and abridged version at ECTA 16.
In these tutorials I make an introduction to what is the cloud and what it means: basically, create applications as loosely connected, polyglot, multi-vendor sets of different programs. Which will spawn a slew of new algorithms, starting with the pool-based evolutionary algorithm we have working on for so long.