Paper Title: A Systematic Evaluation of Evolving Highly Nonlinear Boolean Functions in Odd Sizes
This paper is the result of an exciting collaboration with Claude Carlet (University of Bergen), Marko Đurasević and Domagoj Jakobovic (University of Zagreb) and Stjepan Picek (Radboud University).
The main goal of this work is to investigate the properties of Boolean functions, which play a key role in the design of symmetric cryptographic primitives. The investigation is carried out by comparing various breeds of Evolutionary Algorithms (EAs) to maximize the nonlinearity of Boolean functions. Surprisingly, the experiments highlight that EAs are able to discover Boolean functions of 9 variables with very high nonlinearity scores, an accomplishment that has not been achieved with metaheuristic optimization algorithms so far. This finding could help clarifying the mathematical structure of the space of Boolean functions, where determining the maximum nonlinearity achievable is still an open problem.
Presented at EuroGP, the premier annual conference on Genetic Programming (GP), a specific type of evolutionary algorithm to solve optimization problems by leveraging principles inspired by biological evolution. In particular, EuroGP is the oldest and the only meeting worldwide devoted specifically to this branch of evolutionary computation. The conference is part of EvoStar, the leading European event on Bio‑Inspired AI.
Congratulations Luca!
