Topics 

We welcome submissions on all aspects of multimodal data-driven optimization and learning. Topics of interest include, but are not limited to:

• Evolutionary Algorithms for Multimodal Representation Learning and Fusion

• Neural Architecture Search (NAS) for Multimodal Deep Learning

• Synergy between Evolutionary Computation and Multimodal Foundation Models

• Multimodal Combinatorial Optimization (e.g., Routing, Scheduling, Planning)

• Surrogate-Assisted Optimization with Heterogeneous Data Inputs

• Real-world Applications: Robotics, Healthcare, Smart Manufacturing, and Digital Twins

• Benchmarks and Evaluation Metrics for Multimodal Optimization

• Datasets for Multimodal Optimization