19 citations · 74 across the 14 of their papers we have counts for
9 papers · 1 filter
An investigation on the use of Large Language Models for hyperparameter tuning in Evolutionary Algorithms
Leonardo Lucio Custode, Fabio Caraffini, Anil Yaman +1
Hyperparameter optimization is a crucial problem in Evolutionary Computation. In fact, the values of the hyperparameters directly impact the trajectory taken by the optimization pr…
Collaborative Interactive Evolution of Art in the Latent Space of Deep Generative Models
Ole Hall, Anil Yaman
Generative Adversarial Networks (GANs) have shown great success in generating high quality images and are thus used as one of the main approaches to generate art images. However, u…
Online Distributed Evolutionary Optimization of Time Division Multiple Access Protocols
Anil Yaman, Tim van der Lee, Giovanni Iacca
With the advent of cheap, miniaturized electronics, ubiquitous networking has reached an unprecedented level of complexity, scale and heterogeneity, becoming the core of several mo…
A Framework for Knowledge Integrated Evolutionary Algorithms
Ahmed Hallawa, Anil Yaman, Giovanni Iacca +1
One of the main reasons for the success of Evolutionary Algorithms (EAs) is their general-purposeness, i.e., the fact that they can be applied straightforwardly to a broad range of…
Distributed Embodied Evolution over Networks
Anil Yaman, Giovanni Iacca
In several network problems the optimum behavior of the agents (i.e., the nodes of the network) is not known before deployment. Furthermore, the agents might be required to adapt,…
Novelty Producing Synaptic Plasticity
Anil Yaman, Giovanni Iacca, Decebal Constantin Mocanu +2
A learning process with the plasticity property often requires reinforcement signals to guide the process. However, in some tasks (e.g. maze-navigation), it is very difficult (or i…
Learning with Delayed Synaptic Plasticity
Anil Yaman, Giovanni Iacca, Decebal Constantin Mocanu +2
The plasticity property of biological neural networks allows them to perform learning and optimize their behavior by changing their configuration. Inspired by biology, plasticity c…
Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions
Anil Yaman, Giovanni Iacca, Decebal Constantin Mocanu +3
A fundamental aspect of learning in biological neural networks is the plasticity property which allows them to modify their configurations during their lifetime. Hebbian learning i…
Limited Evaluation Cooperative Co-evolutionary Differential Evolution for Large-scale Neuroevolution
Anil Yaman, Decebal Constantin Mocanu, Giovanni Iacca +2
Many real-world control and classification tasks involve a large number of features. When artificial neural networks (ANNs) are used for modeling these tasks, the network architect…