Call for Papers

Location: York, UK

Dates: 7–9 September 2027

Website: TBC

The 26th UK Workshop on Computational Intelligence (UKCI) invites researchers, academicians, and industry practitioners to submit their latest theoretical advances and real-world applications in bio-inspired and intelligent computing.

As the landscape of artificial intelligence continues to shift toward hybrid, efficient, and reliable systems, UKCI serves as the premier forum for debating the evolution of traditional computational intelligence (CI) paradigms and their intersection with modern foundational AI.

Important dates:

Submission Deadline: 10 May 2027

Notification of Acceptance: 14 June 2027

Camera-Ready Copy Due: 12 July 2027

Early Bird Registration Closes: 19 July 2027

Workshop Dates: 7–9 September 2027

Submission Categories:

Authors are invited to submit original, unpublished work under the following tracks:

• Full Research Papers (6–8 pages): Presenting mature, original research, novel methodologies, or exhaustive empirical evaluations. (To be published in the official proceedings).

• Short / Position Papers (3–4 pages): Highlighting ongoing research, preliminary results, or critical perspectives on current UK AI policy and trends.

• Industrial Case Studies & Demos (2 pages extended abstract): Submissions from industry practitioners focusing on the deployment, scaling, or real-world challenges of CI algorithms in industrial/commercial environments.

Key Track Topics:

We welcome submissions across three primary, interconnected domains, with a particular emphasis this year on sustainability, explainability, and trust.

Track 1: Core CI Paradigms & Hybrid Architectures

While generative models dominate public discourse, the mathematical rigidity of fuzzy systems and the global optimization capabilities of evolutionary algorithms remain vital for creating efficient, verifiable, and hybrid intelligent systems. This track focuses on how bio-inspired algorithms, fuzzy logic, and deep neural networks are evolving alongside contemporary Large Language Models (LLMs), foundation models and agentic AI.

• Evolutionary Computation: Genetic algorithms, genetic programming, swarm intelligence, ant colony optimization, multi-objective evolutionary optimization, and generative AI.

• Fuzzy Systems: Fuzzy logic, neuro-fuzzy systems, fuzzy decision-making, and handling epistemic uncertainty in deep networks.

• Neural Computing: Deep learning architectures, physics-informed neural networks (PINNs), spiking neural networks, transformer architectures, and hardware-accelerated learning.

• Hybrid Systems: Neuro-symbolic AI, evolutionary-deep learning hybrids, quantum machine learning, and memetic algorithms.

Track 2: Trustworthy, Safe, & Explainable CI

Computational intelligence approaches offer unique pathways to interpretability. This track explores how neuro-fuzzy architectures, symbolic AI, and evolutionary optimization can bridge the gap between high-performance machine learning and human-understandable, highly secure decision-making.

• Explainable AI (XAI): Utilizing fuzzy systems, symbolic models, and transparent architectures to demystify complex AI pipelines.

• Safety & Verification: Verifiable optimization, adversarial robustness in CI, and safety-critical alignment using evolutionary constraints.

• Fairness & Ethics: Algorithmic bias mitigation, privacy-preserving CI (federated learning, differential privacy), and governance.

Track 3: Applied CI & Industrial Impact

This track aims at highlighting how CI is solving immediate problems in engineering, aerospace, healthcare, and green energy grid optimization, for example. Moving from theoretical benchmarks to complex environments. Highlighting innovations in multi-agent robotics, intelligent data engineering, and sustainable computing infrastructures.

• Smart Infrastructure & Energy: Net-zero grid optimization, sustainable transport, and smart city modelling.

• Healthcare & Life Sciences: Bioinformatics, clinical decision support systems, and medical image processing using bio-inspired models.

• Robotics & Autonomous Systems: Multi-agent coordination, reinforcement learning in complex environments, computer vision, and edge-computing implementation.

Submission Guidelines:

• Format: All submissions must be written in English and formatted according to the [TBC] LaTeX or MS Word templates available on the workshop website.

• Review Process: The workshop will employ a double-blind review process. Authors must anonymise their submissions by removing names, affiliations, and explicit self-references.

• Submission Portal: Papers must be submitted electronically in PDF format via EasyChair by midnight BST on the deadline date.