Keynote Speakers

Abstract
Imagine computer vision systems that evolve their own architectures, optimize their parameters, and discover novel image features—all without explicit human design. This is the promise of evolutionary computation (EC), a family of algorithms inspired by natural selection. In this talk, we explore how EC empowers computer vision by automating neural architecture design, fine-tuning hyperparameters, and even optimizing preprocessing strategies. From neuroevolution to genetic programming, evolutionary approaches are reshaping how visual intelligence is created and improved. We will examine practical applications, including evolving convolutional and transformer-based vision models, discovering task-specific filters, and integrating EC with deep learning pipelines for greater adaptability and efficiency. The session concludes with a look at emerging directions where evolution-driven AI continuously adapts to new data and environments—paving the way for the next generation of self-evolving vision systems.

Biography
Seyed Jalaleddin Mousavirad (Senior Member, IEEE) is an Assistant Professor at Mid Sweden University, Sundsvall, Sweden. Prior to this, he was a Postdoctoral Researcher at Mid Sweden University and a Postdoctoral Research Fellow at the University of Beira Interior, Portugal, where he contributed to the European project GreenStamp. He has also gained international research experience as a visiting scholar with a leading research group at Xi’an Jiaotong–Liverpool University, Suzhou, China. He is the author of one book, nine book chapters, and more than 180 papers published in international journals and conference proceedings. His research interests include machine learning, evolutionary computation, computer vision, applied artificial intelligence, and measurement systems.