WCCI 2026 — IEEE World Congress on Computational Intelligence
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Call for Papers: Evolutionary Computation for Generative Natural Language Processing

Scope & Aim

Natural Language Processing (NLP) has been transformed by Generative AI and large-scale language models (LLMs) such as GPT, LLaMA, and PaLM, which now underpin applications in dialogue systems, question answering, summarization, and translation. Despite their success, critical challenges remain unresolved: model architecture optimization, interpretability, robustness, and computational efficiency. Evolutionary Algorithms (EAs)—with their adaptability, global search capabilities, and problem-agnostic design—are uniquely positioned to complement or surpass gradient-based methods in tackling these issues. The integration of EAs into NLP research represents a timely and necessary response to the limitations of current approaches.

The main objectives of this session are: to explore evolutionary approaches for optimizing architectures and hyperparameters of LLMs and transformer-based models; to address multi-objective trade-offs in NLP (e.g., accuracy vs. efficiency vs. interpretability vs. fairness); to advance evolutionary prompt engineering for generative models, especially in zero-shot and few-shot settings; to leverage EAs in data-centric NLP, including synthetic data generation, augmentation, and adversarial robustness; to promote interpretable and symbolic modeling in NLP through genetic programming and grammatical evolution; and to foster cross-disciplinary exchange between the evolutionary computation and NLP communities.

This session will be among the first to systematically bridge evolutionary computation and generative NLP, addressing pressing challenges such as emergent behavior control, robust evaluation, and uncertainty quantification in LLMs. Unlike prior optimization-focused work, this session emphasizes multi-objective design, interpretability, and data-centric approaches—areas that remain underexplored in mainstream NLP research.

The session aims to inspire novel frameworks and algorithms that expand the applicability of EAs in large-scale NLP systems; provide practical solutions to real-world NLP problems in low-resource, high-stakes, and multilingual settings; enhance responsible AI development by improving interpretability, fairness, and robustness of generative models; and create a sustainable research community at the EC–NLP interface, potentially shaping future collaborations, projects, and applications in academia and industry.

IEEE CEC 2026 has long been a premier venue for showcasing advances in evolutionary computation. With the rise of generative AI, there is an urgent need to investigate how EAs can contribute to NLP, a domain that now defines much of the global AI landscape. This session directly aligns with CEC’s themes of optimization, hybridization, and real-world impact, while opening a new research frontier at the intersection of EC and NLP.

Topics

  • Evolutionary optimization for NLP model architecture design (e.g., transformers, RNNs, LLM variants)
  • Neuroevolution for learning interpretable or task-specific language models
  • Multi-objective evolutionary algorithms for balancing performance, explainability, fairness, and efficiency in NLP
  • Genetic programming for symbolic and interpretable NLP systems
  • Evolutionary strategies for fine-tuning, transfer learning, and domain adaptation in language models
  • Prompt evolution and automated prompt engineering for generative NLP tasks
  • Evolutionary hyperparameter tuning in transformer-based and generative models
  • EA-driven optimization in low-resource, multilingual, or cross-lingual NLP
  • Evolutionary algorithms for sequence-to-sequence learning (e.g., machine translation, text generation)
  • EA-based methods for syntactic and semantic parsing
  • Evolutionary approaches to named entity recognition, POS tagging, and morphological analysis
  • Genetic and swarm intelligence algorithms for text summarization (extractive/abstractive)
  • EA-based document clustering, topic modeling, and latent semantic analysis
  • Evolutionary information retrieval and question answering systems
  • Automatic text classification and sentiment/emotion analysis using EAs
  • Dialogue systems and conversational AI optimized via evolutionary methods
  • EA-enhanced data augmentation and synthetic text generation
  • Swarm intelligence (e.g., PSO, ACO) for document ranking and query optimization
  • Memetic algorithms for hybrid neural-symbolic NLP frameworks
  • Evolutionary meta-learning and few-shot learning for NLP
  • Evolutionary strategies for aligning LLMs with human preferences
  • Automatic Question Generation (QG) via Evolutionary Optimization
  • Trustworthy NLP systems via evolutionary explainability and fairness modeling
  • Real-world applications of EAs in legal, healthcare, education, and policy-oriented NLP
  • Benchmarking and evaluation frameworks for EA-based NLP solutions
  • Multimodal NLP: Image-Text, Speech-Text, and Video-Text Integration via EA
  • Evolutionary Algorithms for Dialogue State Tracking and Response Ranking
  • Narrative Understanding and Story Generation with EA-Driven Planning
  • Feature Selection and Dimensionality Reduction for Text Classification and Clustering
  • Bias Detection and Fairness Optimization in NLP Models via Evolutionary Strategies

Page Limitation and Submission Format

All papers must be submitted using the IEEE conference proceedings template with body text in 10pt type. LaTeX users must use \documentclass[10pt,conference]{IEEEtran}.

The page limitations are as follows:

  • Full papers: papers of up to 6 pages. A maximum of two extra content pages is allowed (i.e., up to 8 pages), at an additional charge per extra page as specified in the registration page.
  • Journal to Conference (J2C): TBD.

Program Committee

Organizers

Biography of the organisers

Dr. Diego Oliva received his B.S. degree in Electronics and Computer Engineering from the Industrial Technical Education Center (CETI) of Guadalajara, Mexico, in 2007, his M.Sc. degree in Electronic Engineering and Computer Sciences from the University of Guadalajara, Mexico, in 2010, and completed his Ph.D. in Informatics at the Complutense University of Madrid in 2015. Currently, he is an associate professor at the University of Guadalajara in Mexico. His current research interests include computer vision, image processing, artificial intelligence, and metaheuristic optimization algorithms.

Dr. Naveen Saini is currently working as an Assistant Professor in the Department of Information Technology, Indian Institute of Information Technology Allahabad, Uttar Pardesh. Prior to this, he was associated with the Department of Computer Science at Indian Institute of Information Technology Lucknow as an Assistant Professor. He has also worked as a researcher at 4IR Applied Research Center and Assistant Professor at Endicott College of International Studies, Woosong University, South Korea. He did his post doctorate from IRIT (Institut De Recherche En Informatique De Toulouse) and earned his PhD from the Department of Computer Science and Engineering at Indian Institute of Technology Patna (IITP), India. His current research interests include Text Analytics, Social Media Analysis, Multimodal Information Processing, Artificial Intelligence, Machine Learning, Multi-objective Optimization, and Evolutionary Algorithms.

Important Dates

  • January 31, 2026: Paper submission deadline (23:59 Anywhere on Earth, UTC-12)
  • March 15, 2026: Paper acceptance notification
  • April 15, 2026: Camera-ready paper deadline
  • June 21, 2026: Tutorial
  • June 24, 2026: Industry Day
  • June 22–26, 2026: Main Conference

Note about Topics

Because of the wide scope of NLP, some important topics that fit in the scope of the special session may not be listed above. Therefore, if you are unsure whether your work would fit, we encourage you to get in touch with any organizer. All papers must comply with the basic requirements of WCCI 2026. The review process will comply with the standard review process. Each paper will receive at least three reviews from experts in the field. As per our knowledge, there is no previous special session held anywhere as most of the NLP community focuses on using deep learning-based methods.