Search Results for author: Anil Yaman

Found 12 papers, 4 papers with code

Collaborative Interactive Evolution of Art in the Latent Space of Deep Generative Models

1 code implementation28 Mar 2024 Ole Hall, Anil Yaman

In this work, we first employ GANs that are trained to produce creative images using an architecture known as Creative Adversarial Networks (CANs), then, we employ an evolutionary approach to navigate within the latent space of the models to discover images.

Image Generation Navigate

Evolving generalist controllers to handle a wide range of morphological variations

1 code implementation18 Sep 2023 Corinna Triebold, Anil Yaman

Neuro-evolutionary methods have proven effective in addressing a wide range of tasks.

The emergence of division of labor through decentralized social sanctioning

no code implementations10 Aug 2022 Anil Yaman, Joel Z. Leibo, Giovanni Iacca, Sang Wan Lee

Here we show that by introducing a model of social norms, which we regard as emergent patterns of decentralized social sanctioning, it becomes possible for groups of self-interested individuals to learn a productive division of labor involving all critical roles.

Online Distributed Evolutionary Optimization of Time Division Multiple Access Protocols

no code implementations27 Apr 2022 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 modern applications such as smart industry, smart buildings and smart cities.

Meta-control of social learning strategies

1 code implementation18 Jun 2021 Anil Yaman, Nicolas Bredeche, Onur Çaylak, Joel Z. Leibo, Sang Wan Lee

Based on these findings, we hypothesized that meta-control of individual and social learning strategies provides effective and sample-efficient learning in volatile and uncertain environments.

A Framework for Knowledge Integrated Evolutionary Algorithms

no code implementations31 Mar 2021 Ahmed Hallawa, Anil Yaman, Giovanni Iacca, Gerd Ascheid

Notably, the KIEA framework is EA-agnostic (i. e., it works with any evolutionary algorithm), problem-independent (i. e., it is not dedicated to a specific type of problems), expandable (i. e., its knowledge base can grow over time).

Evolutionary Algorithms

Topological Insights into Sparse Neural Networks

3 code implementations24 Jun 2020 Shiwei Liu, Tim Van der Lee, Anil Yaman, Zahra Atashgahi, Davide Ferraro, Ghada Sokar, Mykola Pechenizkiy, Decebal Constantin Mocanu

However, comparing different sparse topologies and determining how sparse topologies evolve during training, especially for the situation in which the sparse structure optimization is involved, remain as challenging open questions.

Distributed Embodied Evolution over Networks

no code implementations28 Mar 2020 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.

Novelty Producing Synaptic Plasticity

no code implementations10 Feb 2020 Anil Yaman, Giovanni Iacca, Decebal Constantin Mocanu, George Fletcher, Mykola Pechenizkiy

A learning process with the plasticity property often requires reinforcement signals to guide the process.

Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions

no code implementations2 Apr 2019 Anil Yaman, Giovanni Iacca, Decebal Constantin Mocanu, Matt Coler, George Fletcher, Mykola Pechenizkiy

Hebbian learning is a biologically plausible mechanism for modeling the plasticity property in artificial neural networks (ANNs), based on the local interactions of neurons.

Learning with Delayed Synaptic Plasticity

no code implementations22 Mar 2019 Anil Yaman, Giovanni Iacca, Decebal Constantin Mocanu, George Fletcher, Mykola Pechenizkiy

Inspired by biology, plasticity can be modeled in artificial neural networks by using Hebbian learning rules, i. e. rules that update synapses based on the neuron activations and reinforcement signals.

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