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Found 12 papers, 4 papers with code
Collaborative Interactive Evolution of Art in the Latent Space of Deep Generative Models
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.
Evolving generalist controllers to handle a wide range of morphological variations
Neuro-evolutionary methods have proven effective in addressing a wide range of tasks.
The emergence of division of labor through decentralized social sanctioning
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
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
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
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).
Topological Insights into Sparse Neural Networks
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
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
A learning process with the plasticity property often requires reinforcement signals to guide the process.
Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions
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
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.
Limited Evaluation Cooperative Co-evolutionary Differential Evolution for Large-scale Neuroevolution
Many real-world control and classification tasks involve a large number of features.
