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Biomimetics
Special Issues
Bio-Inspired AI: When Generative AI and Biomimicry Overlap
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Bio-Inspired AI: When Generative AI and Biomimicry Overlap

A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Biological Optimisation and Management".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 2589

Special Issue Editors

Dr. Haoyu Zuo

E-Mail Website
Guest Editor
Dyson School of Design Engineering, Imperial College London, London, UK
Interests: bio-inspired design; design knowledge; human-AI interaction
Dr. Yuan Yin

E-Mail Website
Guest Editor
Dyson School of Design Engineering, Imperial College London, London, UK
Interests: bio-inspired design; human–AI co-creation; creative cognition; sustainable systems
Dr. Ji Han

E-Mail Website
Guest Editor
INDEX, University of Exeter, Exeter SE1 7TY, UK
Interests: design sustainability; design creativity; AI for design; data-driven design
Special Issues, Collections and Topics in MDPI journals
Dr. Liuqing Chen

E-Mail Website
Guest Editor
College of Computer Science and Technology, Zhejiang University, Hangzhou 310030, China
Interests: bio-inspired design; human-computer interaction; generative AI; machine learning in design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Generative artificial intelligence (AI) is increasingly influencing design, engineering, and research, while bio-inspired design continues to provide effective strategies by translating principles from nature into creative solutions. This Special Issue focuses on the intersection of these two areas, exploring how bio-inspired design can inform the development of AI methods and how generative AI can support bio-inspired design and innovation.

We invite contributions that include theoretical studies, computational models, empirical research, and case studies. Topics of interest include the following: bio-inspired algorithms, generative AI in design and engineering, human–AI co-creation, computational creativity informed by natural systems, and sustainable innovations supported by AI and bio-inspired design.

The aim of this Special Issue is to provide a platform for interdisciplinary research connecting artificial intelligence and bio-inspired design. By collecting recent work in this emerging area, we hope to highlight current progress, identify new opportunities, and encourage collaboration across design, AI, and nature-inspired research communities.

We look forward to receiving your contributions.

Dr. Haoyu Zuo
Dr. Yuan Yin
Dr. Ji Han
Dr. Liuqing Chen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomimetics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • bio-inspired design
  • generative artificial intelligence
  • design
  • biomimetics
  • biomimicry

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Published Papers (3 papers)

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21 pages, 2947 KB  
Article
HFSOF: A Hierarchical Feature Selection and Optimization Framework for Ultrasound-Based Diagnosis of Endometrial Lesions
by Yongjun Liu, Zihao Zhang, Tongyu Chai and Haitong Zhao
Biomimetics 2026, 11(1), 74; https://doi.org/10.3390/biomimetics11010074 - 15 Jan 2026
Viewed by 475
Abstract
Endometrial lesions are common in gynecology, exhibiting considerable clinical heterogeneity across different subtypes. Although ultrasound imaging is the preferred diagnostic modality due to its noninvasive, accessible, and cost-effective nature, its diagnostic performance remains highly operator-dependent, leading to subjectivity and inconsistent results. To address [...] Read more.
Endometrial lesions are common in gynecology, exhibiting considerable clinical heterogeneity across different subtypes. Although ultrasound imaging is the preferred diagnostic modality due to its noninvasive, accessible, and cost-effective nature, its diagnostic performance remains highly operator-dependent, leading to subjectivity and inconsistent results. To address these limitations, this study proposes a hierarchical feature selection and optimization framework for endometrial lesions, aiming to enhance the objectivity and robustness of ultrasound-based diagnosis. Firstly, Kernel Principal Component Analysis (KPCA) is employed for nonlinear dimensionality reduction, retaining the top 1000 principal components. Secondly, an ensemble of three filter-based methods—information gain, chi-square test, and symmetrical uncertainty—is integrated to rank and fuse features, followed by thresholding with Maximum Scatter Difference Linear Discriminant Analysis (MSDLDA) for preliminary feature selection. Finally, the Whale Migration Algorithm (WMA) is applied to population-based feature optimization and classifier training under the constraints of a Support Vector Machine (SVM) and a macro-averaged F1 score. Experimental results demonstrate that the proposed closed-loop pipeline of “kernel reduction—filter fusion—threshold pruning—intelligent optimization—robust classification” effectively balances nonlinear structure preservation, feature redundancy control, and model generalization, providing an interpretable, reproducible, and efficient solution for intelligent diagnosis in small- to medium-scale medical imaging datasets. Full article
(This article belongs to the Special Issue Bio-Inspired AI: When Generative AI and Biomimicry Overlap)
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35 pages, 1515 KB  
Article
Bio-RegNet: A Meta-Homeostatic Bayesian Neural Network Framework Integrating Treg-Inspired Immunoregulation and Autophagic Optimization for Adaptive Community Detection and Stable Intelligence
by Yanfei Ma, Daozheng Qu and Mykhailo Pyrozhenko
Biomimetics 2026, 11(1), 48; https://doi.org/10.3390/biomimetics11010048 - 7 Jan 2026
Viewed by 749
Abstract
Contemporary neural and generative architectures are deficient in self-preservation mechanisms and sustainable stability. In uncertain or noisy situations, they frequently demonstrate oscillatory learning, overconfidence, and structural deterioration, indicating a lack of biological regulatory principles in artificial systems. We present Bio-RegNet, a meta-homeostatic Bayesian [...] Read more.
Contemporary neural and generative architectures are deficient in self-preservation mechanisms and sustainable stability. In uncertain or noisy situations, they frequently demonstrate oscillatory learning, overconfidence, and structural deterioration, indicating a lack of biological regulatory principles in artificial systems. We present Bio-RegNet, a meta-homeostatic Bayesian neural network architecture that integrates T-regulatory-cell-inspired immunoregulation with autophagic structural optimization. The model integrates three synergistic subsystems: the Bayesian Effector Network (BEN) for uncertainty-aware inference, the Regulatory Immune Network (RIN) for Lyapunov-based inhibitory control, and the Autophagic Optimization Engine (AOE) for energy-efficient regeneration, thereby establishing a closed energy–entropy loop that attains adaptive equilibrium among cognition, regulation, and metabolism. This triadic feedback achieves meta-homeostasis, transforming learning into a process of ongoing self-stabilization instead of static optimization. Bio-RegNet routinely outperforms state-of-the-art dynamic GNNs across twelve neuronal, molecular, and macro-scale benchmarks, enhancing calibration and energy efficiency by over 20% and expediting recovery from perturbations by 14%. Its domain-invariant equilibrium facilitates seamless transfer between biological and manufactured systems, exemplifying a fundamental notion of bio-inspired, self-sustaining intelligence—connecting generative AI and biomimetic design for sustainable, living computation. Bio-RegNet consistently outperforms the strongest baseline HGNN-ODE, improving ARI from 0.77 to 0.81 and NMI from 0.84 to 0.87, while increasing equilibrium coherence κ from 0.86 to 0.93. Full article
(This article belongs to the Special Issue Bio-Inspired AI: When Generative AI and Biomimicry Overlap)
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17 pages, 1089 KB  
Article
Bio-Inspired Neural Network Dynamics-Aware Reinforcement Learning for Spiking Neural Network
by Yu Zheng, Jingfeng Xue, Junhan Yang and Yanjun Zhang
Biomimetics 2026, 11(1), 47; https://doi.org/10.3390/biomimetics11010047 - 7 Jan 2026
Viewed by 776
Abstract
Artificial Intelligence (AI) has seen rapid advancements in recent times, finding applications across various sectors and achieving notable successes. However, current AI models based on Deep Convolutional Neural Networks (DNNs) face numerous challenges, particularly a lack of interpretability, which severely restricts their future [...] Read more.
Artificial Intelligence (AI) has seen rapid advancements in recent times, finding applications across various sectors and achieving notable successes. However, current AI models based on Deep Convolutional Neural Networks (DNNs) face numerous challenges, particularly a lack of interpretability, which severely restricts their future potential. Spiking Neural Networks (SNNs), considered the third generation of Artificial Neural Networks (ANNs), are at the forefront of brain-inspired AI research. The resemblance between SNNs and biological neural networks offers the potential to create more human-like AI systems with enhanced interpretability, paving the way for more trustworthy AI implementations. Despite this promise, the absence of efficient training methods for large-scale and complex SNNs hampers their broader application. This paper investigates bio-inspired reinforcement learning strategies by examining neural network dynamics during SNN training. The aim is to improve learning efficiency and effectiveness for extensive and intricate SNNs. Our findings suggest that using reinforcement learning to focus on neural network dynamics may be a promising approach for developing learning algorithms for future large-scale SNNs. Full article
(This article belongs to the Special Issue Bio-Inspired AI: When Generative AI and Biomimicry Overlap)
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