[CfP] Last Call – EGML-EC GECCO 2023 workshop on Enhancing Generative Machine Learning with Evolutionary Computation

Dear Colleague(s),

Below you can find the last call for papers for the 2nd Workshop on
Enhancing Generative Machine Learning with Evolutionary Computation.

Feel free to distribute, and thank you for your time.

Best regards,

João Correia




2nd Workshop on Enhancing Generative Machine Learning with Evolutionary


Genetic and Evolutionary Computation Conference (GECCO’23)

Lisbon, Portugal, July 15 to 19, 2023

Overview and Scope

Generative Machine Learning has become a key field in machine learning and
deep learning. In recent years, this field of research has proposed many
deep generative models (DGMs) that range from a broad family of methods
such as generative adversarial networks (GANs), variational autoencoders
(VAEs), autoregressive (AR) models and stable diffusion models (SD). These
models combine advanced deep neural networks with classical density
estimation (either explicit or implicit) for mainly generating synthetic
data samples. Although these methods have achieved state-of-the-art results
in the generation of synthetic data of different types, such as images,
speech, text, molecules, video, etc., Deep generative models are still
difficult to train.

There are still open problems, such as the vanishing gradient and mode
collapse in DGMs, which limit their performance. Although there are
strategies to minimize the effect of those problems, they remain
fundamentally unsolved. In recent years, evolutionary computation (EC) and
related bio-inspired techniques (e.g. particle swarm optimization) and in
the form of Evolutionary Machine Learning approaches have been successfully
applied to mitigate the problems that arise when training DGMs, leveraging
the quality of the results to impressive levels. Among other approaches,
these new solutions include GAN, VAE, AR, and SD training methods or fine
tuning optimization based on evolutionary and coevolutionary algorithms,
the combination of deep neuroevolution with training approaches, and the
evolutionary exploration of latent space.

This workshop aims to act as a medium for debate, exchange of knowledge and
experience, and encourage collaboration for researchers focused on DGMs and
the EC community. Bringing these two communities together will be essential
for making significant advances in this research area. Thus, this workshop
provides a critical forum for disseminating the experience on the topic of
enhancing generative modelling with EC, presenting new and ongoing research
in the field, and to attract new interest from our community.

Topics of Interest

Particular topics of interest are (not exclusively):

· Evolutionary and co-evolutionary algorithms to train deep generative

· EC-based optimization of hyper-parameters for deep generative models;

· Neuroevolution applied to train deep generative architectures

· Dynamic EC-based evolution of deep generative models training

· Evolutionary latent space exploration

· Real-world applications of EC-based deep generative models solutions

· Multi-criteria adversarial training of deep generative models

· Evolutionary generative adversarial learning models

· Software libraries and frameworks for deep generative models applying

All accepted papers of this workshop will be included in the Proceedings of
the Genetic and Evolutionary Computation Conference (GECCO’23) Companion

Important dates

Submission opening: February 13, 2023

Submission deadline: April 14, 2023

Acceptance notification: May 3, 2023

Camera-ready and registration: May 10, 2023

Workshop date: TBC depending on GECCO program schedule (July 15 or 19, 2023)

There will be NO EXTENSIONS to any of the deadlines

Instructions for Authors

We invite submissions of two types of paper:

· Regular papers (limit 8 pages)

· Short papers (limit 4 pages)

Papers should present original work that meets the high-quality standards