Multimodal Data-Driven Optimization and Learning

July 13-17 , 2026  

San Antonio de Belén, Alajuela, Costa Rica

https://sites.google.com/view/gecco26-multidol/home

Overview

Multimodal Data-Driven Optimization and Learning (MultiDOL) workshop focuses on addressing the growing need for integrating diverse data modalities—such as images, text, and sensor data—into evolutionary learning and optimisation frameworks. As real-world problems increasingly involve heterogeneous data sources, from autonomous systems combining visual and LiDAR data to healthcare applications fusing medical images and clinical records, traditional learning and optimization approaches must evolve.

This workshop explores novel methods for multimodal data integration in evolutionary algorithms, including multimodal representation learning, cross-modal feature fusion, and hybrid EC-deep learning approaches. We welcome contributions on theoretical foundations, algorithm design, benchmark development, and applications across areas, such as robotics, smart manufacturing, healthcare, and environmental monitoring. We brings together researchers from evolutionary computation, machine learning, computer vision, and application domains to address key questions: How can evolutionary algorithms effectively process information from multiple data modalities? What new optimization paradigms emerge when combining EC with modern multimodal AI including foundation models? How do we design appropriate benchmarks for these problems?

MultiDOL aims to foster collaborations between the EC community and multimodal AI researchers, establishing a foundation for sustained research in this emerging interdisciplinary area.

Topics 

We welcome submissions on all aspects of multimodal data-driven optimization. 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

   

Keynote Speaker :

 

Talk Title:  Evolutionary Multi-modal Learning and Optimization

 

Mengjie Zhang, Ph.D., FRSNZ, FIEEE, FEngNZ,

Professor,
Victoria University of Wellington, New Zealand

 

Invited Speakers:

Talk Title: Automating Multimodal Machine Learning Using Optimisation   Nelishia Pillay, Ph.D., Professor, University of Pretoria, South Africa

Talk Title: Multimodal Learning and lts Applications in Brain Medicine   Lifang He, Ph.D., Associate Professor, Lehigh University, USA

Talk Title: Genetic Programming for Multimodal Machine Learning   Ying Bi, Ph.D., Professor, Zhengzhou University, China

Additional invited speakers to be announced...

Paper Submission:

Publication Policy

All accepted workshop papers will be published in the GECCO Companion Proceedings and included in the ACM Digital Library.

Paper Specifications

Interested participants are invited to submit full papers adhering to the following constraints:

• Paper Type: Full Papers

• Abstract Length: Maximum 200 words

• Page Limit: Maximum 8 pages (excluding references)

• Anonymity: All submissions must be ANONYMIZED for the double-blind review process.

Submission Format

All submissions must follow the official GECCO 2026 formatting guidelines (ACM Template).

Submission Site

Papers must be submitted via the GECCO paper submission website (select "Workshops" track and choose "Multimodal Data-Driven Optimization and Learning (MultiDOL)").

Review Process

All submitted papers will undergo a rigorous double-blind review process by the program committee.

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Important Dates:

• Submission Deadline: April 3, 2026
• Author Notification: April 24, 2026
• Early Registration Deadline: May 11, 2026
• Camera-Ready Deadline: TBD
• Workshop Date: TBD

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Organizing Committee Co-Chairs:

Xueming Yan, Ph.D., Professor, SMIEEE, Guangdong University of Foreign Studies

(Email: yanxm@gdufs.edu.cn)

 

Bing Xue, Ph.D., Professor, FIEEE, FEngNZ, Victoria University of Wellington

(Email: bing.xue@ecs.vuw.ac.nz)

 

Yaochu Jin, Ph.D., Chair Professor of AI, MAE, FIEEE, Westlake University

(Email: jinyaochu@westlake.edu.cn)

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Affiliated Laboratory:

Trustworthy and General Artificial Intelligence Laboratory (TGAI)

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可信及通用人工智能實驗室（TGAI）

金耀初實驗室（可信及通用人工智能實驗室）同時致力于應用驅動的可信人工智能研究及其在工業(yè)、科學和藝術中的應用，以及采用演化發(fā)育方法探索實現通用人工智能的新途徑。主要研究方向包括：

1) 可信人工智能方向: 安全、隱私保護及公平的數據驅動的優(yōu)化與學習；基于圖神經網絡及擴散模型的優(yōu)化與學習；基于大模型的通用優(yōu)化與決策；大模型自動驗證；

2) 類腦具身智能方向: 大規(guī)模類腦脈沖神經網絡；具身智能系統(tǒng)的自主演進；具身安全與具身大模型；具身系統(tǒng)的控制與形態(tài)的協(xié)同發(fā)育與演化；

3) AI for Science & Art方向: 人工智能納米材料-蛋白質/植物-環(huán)境互作；人工智能醫(yī)學診斷/康復；人工智能藝術診療。

 

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