New methods to construct adequate Boolean functions for use in cryptography have had to evolve in accordance with the requirements of continually proposed cryptosystems. Among the desired properties in Boolean functions are balancedness, high non-...

Constrained single-objective problems have been frequently tackled by evolutionary multi-objective algorithms where the constraint is relaxed into an additional objective. Recently, it has been shown that Pareto optimization approaches using bi-...

Constrained submodular optimization problems play a key role in the area of combinatorial optimization as they capture many NP-hard optimization problems. So far, Pareto optimization approaches using multi-objective formulations have been shown to ...$\mathrm{}\mathrm{}$$\mathrm{}$$\mathrm{}\mathrm{}$

Variants of the GSEMO algorithm using multi-objective formulations have been successfully analyzed and applied to optimize chance-constrained submodular functions. However, due to the effect of the increasing population size of the GSEMO algorithm ...

Deep neural networks for medical image segmentation often require data from multiple medical institutions, but privacy concerns limit data sharing, making federated learning (FL) a viable alternative. However, predefined network architectures in ...

Hyperparameter optimization is a crucial problem in Evolutionary Computation. In fact, the values of the hyperparameters directly impact the trajectory taken by the optimization process, and their choice requires extensive reasoning by human operators. ...

de novo drug design (dnDD) aims to generate novel molecules that meet several conflicting objectives, positioning it as a quintessential many-objective optimization problem (MaOP), where more than three objectives must be simultaneously optimized. This ...

This paper concerns explaining session-based recommendations using Grammatical Evolution. A session-based recommender system processes a given sequence of products browsed by a user and suggests the most relevant next product to display to the user. ...

In the rapidly evolving field of machine learning, adversarial attacks pose a significant threat to the robustness and security of models. Amongst these, decision-based attacks are particularly insidious due to their nature of requiring only the model's ...

This paper presents an empirical study of the combination of surrogate models with the JADE algorithm. It focuses on the connection between the accuracy of surrogate models and optimization efficiency. Our study uses surrogate models to approximate ...

The emergence of quantum computers represents a crucial leap forward in practical computability, when compared to classical architectures. Harnessing that power effectively is an exercise of increasing importance. Despite research in this field expanding ...

We address the placement of emergency exits to facilitate evacuation from supermarket-like environments. Using a simulation-based approach, evolutionary algorithms are shown to provide good results compared to a greedy heuristic and a numerical method.

We analyze the energy consumption of running evolutionary algorithms in batch as a function of the rest time between runs. It is shown that energy consumption can be reduced by 5%-8% by inserting short pauses between runs to reduce hysteretic phenomena.

The Automated Design Competition in GECCO'24 aims to find intelligent 3D agents that perform better in specific environments. To address this problem, this technical report proposes a Cascaded Structure and Parameter Optimization (CaSPO) framework. After ...

Final productive fitness is an a posteriori fitness estimate for evolutionary algorithms that takes into account the fitness of an individual's descendants. We use that metric in the context of surrogate-based evolutionary algorithms to show that ...

Advances in Large Language Models (LLMs) have led them to be used as black boxes in several evolutionary algorithms for program synthesis. While these methods tend to be agnostic about which model is used, they only allow for using one. This paper ...

As pathogens are rapidly evolving to evade modern drugs, it is vital to design efficient, optimal treatments in a timely manner. One way to achieve this is by mimicking the process of evolution when designing drugs most fit to combat pathogens of ...

This work investigates the evolution of Boolean functions represented with the algebraic normal form (ANF) representation. This novel direction allows for a better "fit" between the representations and properties but also presents some challenges. Our ...

In evolutionary computation, diploid genotypes (i.e., genotypes with two chromosomes) are traditionally used mostly in the context of optimization of non-stationary problems. Recent research, however, suggested that the use of diploid genotypes with ...

The Split Delivery Vehicle Routing Problem with Three-Dimensional Loading Constraints (3L-SDVRP) combines routing and packing challenges with two objectives (minimizing travel distance and maximizing vehicle loading rate). Meta-heuristic algorithms are ...