INF421: Evolutionary Algorithms project

Programming project from the course INF421: Design and Analysis of Algorithms at École Polytechnique. The problem statement is provided.

Overview

In this project we consider the pseudo-boolean optimization problem. Let $n \in \mathbb{N}^*$ denote a positive integer and $V = \{1, 2, ..., n \} $. We shall consider functions $f : \{0, 1 \}^n \to \mathbb{R}$ in $n$ binary variables $x_1, x_2, ..., x_n$ representing subsets of $V$ (i.e. $x_i = 1$ if element $i$ is in the subset and $x_i = 0$ otherwise). Such functions are called pseudo-boolean functions or set functions.

Our goal is to compute $$x^* = \mathrm{arg opt}_{x \in \{0, 1 \}^n} f(x)$$ using the $(\mu + \lambda)$ evolutionary strategy for classical benchmark functions OneMax, LeadingOnes and Jumpk. For further details, please check the project report.

Requirements and how to run locally

The project was implemented using the Python programming language. A requirements.txt file with all project dependencies is provided.

All the code can be found in the code folder in the repository.

    cd code

Now, to generate the scatter plots for the empiric runtime analysis of the OneMax and LeadingOnes benchmark functions (as described in task 2) we run the EmpiricRunTimes.py file. The generated plots are saved in the plots folder.

    python EmpiricRunTimes.py

To generate a series of plots for the empirical diversity tests of the Jumpk benchmark function using the $(\mu + \lambda)$ GA, we run the GAtests.py file. The generated plots are saved in a folder inside the plots folder.

    python GAtests.py

Unit tests are also provided in the unit_tests folder.

Useful resources

For further reading: Andrew N. Sloss and Steven Gustafson; 2019 Evolutionary Algorithms Review