IEEE WCCI 2026 Special Session

Integrating Machine Learning Methods into Evolutionary Optimization

Advancing evolutionary algorithms through systematic integration of machine learning techniques for enhanced scalability, efficiency, and robustness

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2026

IEEE WCCI

4

Expert Organizers

ML+EA

Integration Focus

Session Overview

Advancing evolutionary computation through systematic integration of machine learning

The motivation for this special session is to consolidate and accelerate research on the integration of ML within EAs, addressing the demand for algorithms that can scale to expensive, high-dimensional, and structurally diverse problems.

Although this integration has produced clear benefits, current efforts remain fragmented and often tied to specific problem classes or evaluation protocols. This session offers a focused venue to bring these developments together, encourage principled and reproducible methodologies, and foster cross-fertilization between theory, algorithm design, and real-world applications.

Session Objectives

Embed learning in a well-founded manner within the evolutionary workflow

Demonstrate measurable gains in evaluation cost, scalability, and solution quality

Validate gains rigorously on benchmarks and real-world case studies

Machine Learning Integration Techniques

Online Parameter Control

Dynamic adaptation of algorithm parameters during execution

Adaptive Operator Selection

Intelligent selection of genetic operators based on performance

Surrogate-Assisted Optimization

Reducing fitness evaluation costs through ML models

Expected Impact

The impact of this session will be to advance the development of EAs that are more adaptive, efficient, and scalable, reinforcing their role as a central methodology in computational intelligence while broadening their applicability to domains such as sustainable energy, transportation, healthcare, robotics, and data-driven decision-making.

Key Application Domains

Sustainable Energy

Transportation

Healthcare

Robotics

Data Science

Manufacturing

Session Organizers

Leading experts in evolutionary computation and machine learning

Professor Lhassane Idoumghar

Director, IRIMAS Institute

Université de Haute-Alsace, France

Full Professor and Director of IRIMAS Institute since 2020. Expert in evolutionary computation, optimization, AI, and machine learning. Achieved prestigious rank of Exceptional Class 2. Active in major international research projects including ANR, Interreg, PEPR, and Horizon Europe.

Professor Amir H. Gandomi

Professor of Data Science

University of Technology Sydney, Australia

400+ journal papers, 70,000+ citations (H-index=114). Highly Cited Researcher for 6 years. Ranked 24th most impactful researcher in AI & Image Processing (2023). Multiple prestigious awards including 2025 Sigma Xi Young Investigator Award.

Dr. Mahmoud Golabi

Tenured Researcher & Lecturer

Université de Haute-Alsace, France

Ph.D. from Eastern Mediterranean University, Cyprus. Expert in hybrid evolutionary optimization techniques. Focus on integrating machine learning into evolutionary optimization for enhanced efficiency and solution quality.

Dr. Abdennour Azerine

Assistant Professor

Université de Haute-Alsace, France

Ph.D. in Computer Science from Algeria. Specialist in multi-agent scheduling, surrogate-assisted optimization, mathematical modeling for combinatorial optimization, and ML-driven scheduling solutions.

Call for Papers

Submit your research on ML-enhanced evolutionary optimization

Previous Edition Success

We previously organized this special session at IEEE CEC 2025 in Hangzhou, China, receiving 9 submissions with 4 papers accepted and presented. View CEC 2025 Session