19 citations · 74 across the 14 of their papers we have counts for
16 papers
Multi-Task Optimization over Networks of Tasks
Julian Hatzky, Thomas Bartz-Beielstein, A. E. Eiben +1
Multi-task optimization is a powerful approach for solving a large number of tasks in parallel. However, existing algorithms face distinct limitations: Population-based methods sca…
Benefits of Low-Cost Bio-Inspiration in the Age of Overparametrization
Kevin Godin-Dubois, Anil Yaman, Anna V. Kononova
While Central Pattern Generators (CPGs) and Multi-Layer Perceptrons (MLP) are widely used paradigms in robot control, few systematic studies have been performed on the relative mer…
Semantic knowledge guides innovation and drives cultural evolution
Anil Yaman, Shen Tian, Björn Lindström
Cultural evolution allows ideas and technologies to accumulate across generations, reaching their most complex and open-ended form in humans. While social learning enables the tran…
Successful Misunderstandings: Learning to Coordinate Without Being Understood
Nikolaos Kondylidis, Anil Yaman, Frank van Harmelen +2
The main approach to evaluating communication is by assessing how well it facilitates coordination. If two or more individuals can coordinate through communication, it is generally…
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…
The emergence of division of labor through decentralized social sanctioning
Anil Yaman, Joel Z. Leibo, Giovanni Iacca +1
Human ecological success relies on our characteristic ability to flexibly self-organize into cooperative social groups, the most successful of which employ substantial specializati…
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…
Meta-control of social learning strategies
Anil Yaman, Nicolas Bredeche, Onur Çaylak +2
Social learning, copying other's behavior without actual experience, offers a cost-effective means of knowledge acquisition. However, it raises the fundamental question of which in…
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…
Topological Insights into Sparse Neural Networks
Shiwei Liu, Tim Van der Lee, Anil Yaman +5
Sparse neural networks are effective approaches to reduce the resource requirements for the deployment of deep neural networks. Recently, the concept of adaptive sparse connectivit…
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…