We invite original, previously unpublished contributions from researchers, practitioners and educators across the full breadth of evolutionary computation, from theoretical foundations to real world applications.
Paper Topics
The IEEE Congress on Evolutionary Computation (IEEE CEC) is a world-class event in the field of Evolutionary Computation. One of the flagship conferences of the IEEE Computational Intelligence Society (IEEE CIS) and held annually since 1994, it provides a forum to bring together researchers and practitioners from all over the world to present and discuss their research findings on Evolutionary Computation. We invite original, previously unpublished contributions from researchers, practitioners and educators across the full breadth of evolutionary computation, from theoretical foundations to real-world applications.
IEEE CEC 2027 will be held in Edinburgh, Scotland, a vibrant capital renowned for its world-leading universities and research community and, in late July, for the festivals that fill the city each summer. We warmly invite the community to join us for an outstanding scientific programme in an inspiring setting.
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Ant colony optimization and swarm intelligence
Artificial immune systems
Coevolutionary systems
Coevolution
Cultural algorithms
Differential evolution
Estimation of distribution algorithms
Evolutionary computation theory
Evolutionary developmental systems
Evolutionary programming
Evolution strategies
Genetic algorithms
Genetic programming
Heuristics
Interactive evolutionary computation
Learning classifier systems
Memetic algorithms
Multi-meme and hybrid algorithms
Molecular and quantum computing
Multi-objective evolutionary algorithms
Parallel and distributed algorithms
Particle swarm optimization
Representation and operators
Self-adaptation in evolutionary computation
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Automated design of heuristics
Evolutionary fuzzy systems
Evolutionary neural architecture search
Evolutionary reinforcement learning
Fairness-aware optimization
Federated and secure optimization
Large language models for optimization
Metaheuristics and hyper-heuristics
Multi-objective machine learning
Neural optimization
Surrogate-assisted optimization
Algorithm portfolios
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Combinatorial Optimization
Constraint handling
Dynamic optimization
Large-scale optimization
Multi-modal optimization
Multi-objective optimization
Numerical optimization
Optimization in the presence of uncertainty
Robust optimization
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Art and music
Artificial life and adaptive behavior
Autonomous mental and behavior development
Biometrics
Bioinformatics and computational biology
Classification
Clustering
Data mining
Defense and cybersecurity
Drug design
Embodied robotics
Engineering design
Evolutionary games and multi-agent systems
Evolvable hardware
Evolutionary software engineering
Evolutionary robotics
Finance and economics
Intelligent systems applications
Real-world applications
AI ethics and governance
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Causality and evolvability
Convergence
Fitness landscape analysis
Robustness and neutrality
Runtime analysis
Scalability and complexity analysis