no code implementations • 15 Feb 2024 • Claude Carlet, Marko Ðurasevic, Domagoj Jakobovic, Stjepan Picek, Luca Mariot
In the last 30 years, evolutionary algorithms have been shown to be a strong option for evolving Boolean functions in different sizes and with different properties.
no code implementations • 9 Jan 2024 • Luca Mariot, Alberto Leporati, Luca Manzoni
A Particle Swarm Optimizer for the search of balanced Boolean functions with good cryptographic properties is proposed in this paper.
no code implementations • 20 Nov 2023 • Claude Carlet, Marko Ðurasevic, Domagoj Jakobovic, Luca Mariot, Stjepan Picek
This paper provides a detailed experimentation with evolutionary algorithms with the goal of evolving (anti-)self-dual bent Boolean functions.
no code implementations • 20 Nov 2023 • Claude Carlet, Marko Ðurasevic, Bruno Gašperov, Domagoj Jakobovic, Luca Mariot, Stjepan Picek
Rotation symmetric Boolean functions represent an interesting class of Boolean functions as they are relatively rare compared to general Boolean functions.
no code implementations • 26 May 2023 • Fabio Anselmi, Mauro Castelli, Alberto D'Onofrio, Luca Manzoni, Luca Mariot, Martina Saletta
In recent years, a new mutation operator, Geometric Semantic Mutation with Local Search (GSM-LS), has been proposed to include a local search step in the mutation process based on the idea that performing a linear regression during the mutation can allow for a faster convergence to good-quality solutions.
no code implementations • 12 Feb 2023 • Marko Djurasevic, Domagoj Jakobovic, Luca Mariot, Stjepan Picek
By observing the range of possible changes an operator can provide, as well as relative probabilities of specific transitions in the objective space, one can use this information to design a more effective combination of genetic operators.
no code implementations • 25 Jan 2023 • Fiske Schijlen, Lichao Wu, Luca Mariot
Side-channel analysis (SCA) can obtain information related to the secret key by exploiting leakages produced by the device.
no code implementations • 9 Dec 2022 • Marko Djurasevic, Domagoj Jakobovic, Luca Mariot, Sihem Mesnager, Stjepan Picek
One example of such a property is called boomerang uniformity, which helps to be resilient against boomerang attacks.
no code implementations • 21 Nov 2022 • Claude Carlet, Luca Mariot, Luca Manzoni, Stjepan Picek
The design of binary error-correcting codes is a challenging optimization problem with several applications in telecommunications and storage, which has also been addressed with metaheuristic techniques and evolutionary algorithms.
no code implementations • 22 Jun 2022 • Luca Manzoni, Luca Mariot, Eva Tuba
We continue the study of Genetic Algorithms (GA) on combinatorial optimization problems where the candidate solutions need to satisfy a balancedness constraint.
no code implementations • 5 May 2022 • Mauro Castelli, Luca Manzoni, Luca Mariot, Giuliamaria Menara, Gloria Pietropolli
Among the evolutionary methods, one that is quite prominent is Genetic Programming, and, in recent years, a variant called Geometric Semantic Genetic Programming (GSGP) has shown to be successfully applicable to many real-world problems.
no code implementations • 17 Feb 2022 • Claude Carlet, Marko Djurasevic, Domagoj Jakobovic, Luca Mariot, Stjepan Picek
Finding balanced, highly nonlinear Boolean functions is a difficult problem where it is not known what nonlinearity values are possible to be reached in general.
no code implementations • 16 Feb 2022 • Carlos Coello Coello, Marko Djurasevic, Domagoj Jakobovic, Luca Mariot, Stjepan Picek
While there is no reason to doubt the performance of CMA-ES, the lack of comparison with different metaheuristics and results for the challenge-response pair-based attack leaves open questions if there are better-suited metaheuristics for the problem.
no code implementations • 16 Feb 2022 • Luca Mariot, Stjepan Picek, Domagoj Jakobovic, Marko Djurasevic, Alberto Leporati
Finding Boolean functions suitable for cryptographic primitives is a complex combinatorial optimization problem, since they must satisfy several properties to resist cryptanalytic attacks, and the space is very large, which grows super exponentially with the number of input variables.
no code implementations • 25 Nov 2021 • Luca Mariot
We consider the optimization problem of constructing a binary orthogonal array (OA) starting from a bigger one, by removing a specified amount of lines.
no code implementations • 25 Nov 2021 • Luca Mariot, Stjepan Picek, Domagoj Jakobovic, Marko Djurasevic, Alberto Leporati
Combinatorial designs provide an interesting source of optimization problems.
1 code implementation • 3 Jun 2021 • Mauro Castelli, Luca Manzoni, Luca Mariot, Marco S. Nobile, Andrea Tangherloni
In the crowded environment of bio-inspired population-based metaheuristics, the Salp Swarm Optimization (SSO) algorithm recently appeared and immediately gained a lot of momentum.
1 code implementation • 25 May 2021 • Luca Mariot, Stjepan Picek, Domagoj Jakobovic, Alberto Leporati
Reversible Cellular Automata (RCA) are a particular kind of shift-invariant transformations characterized by a dynamics composed only of disjoint cycles.
no code implementations • 24 May 2021 • Lucija Planinic, Marko Djurasevic, Luca Mariot, Domagoj Jakobovic, Stjepan Picek, Carlos Coello Coello
This paper investigates the influence of genotype size on evolutionary algorithms' performance.
no code implementations • 23 Apr 2020 • Luca Manzoni, Domagoj Jakobovic, Luca Mariot, Stjepan Picek, Mauro Castelli
Tasks related to Natural Language Processing (NLP) have recently been the focus of a large research endeavor by the machine learning community.
no code implementations • 23 Apr 2020 • Luca Manzoni, Luca Mariot, Eva Tuba
Experiments show that improving the exploration of the search space with this adaptive bias strategy is beneficial for the GA performances in terms of the number of optimal solutions found for the balanced nonlinear Boolean functions problem.
1 code implementation • 23 Apr 2020 • Domagoj Jakobovic, Luca Manzoni, Luca Mariot, Stjepan Picek, Mauro Castelli
In the second experiment, we train a GP convolutional predictor on two degraded images, removing around 20% of their pixels.
1 code implementation • 23 Apr 2019 • Luca Manzoni, Luca Mariot, Eva Tuba
Furthermore, in two out of three crossovers, the "left-to-right" version performs better than the "shuffled" version.