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Bayesian optimisation (BO) is an efficient approach for solving expensive optimisation problems, where acquisition functions play a major role in achieving the trade-off between exploitation and exploration. The exploitation-exploration trade-off is ...

Transfer optimization enables data-efficient optimization of a target task by leveraging experiential priors from related source tasks. This is especially useful in multiobjective optimization settings where a set of tradeoff solutions is sought under ...

Designing a power plant is a complex process that requires consideration of multiple optimization criteria, such as safety, environmental impact, and efficiency. This paper introduces the use of estimation of distribution algorithms (EDAs) for the ...

This paper introduces a novel data-free model extraction attack that significantly advances the current state-of-the-art in terms of efficiency, accuracy, and effectiveness. Traditional black-box methods rely on using the victim's model as an oracle to ...

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 ...

White blood cells (WBCs) are essential for immune and inflammatory responses, and their precise classification is crucial for diagnosing and managing diseases. Although convolutional neural networks (CNNs) are effective for image classification, ...

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 ...