Bio-Inspired Machine Learning and Evolutionary Computing

A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Development of Biomimetic Methodology".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 87

Special Issue Editor


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Guest Editor
Department of Informatics, University of Piraeus, Karaoli & Dimitriou 80, 18534 Piraeus, Greece
Interests: biologically inspired computing‬; machine learning; pattern recognition; data mining; evolutionary computing; signal processing; digital social networks

Special Issue Information

Dear Colleagues,

The Special Issue Bio-Inspired Machine Learning and Evolutionary Computing explores computational paradigms inspired by natural and biological systems to address complex problems in optimization, learning, and adaptation. Drawing from evolutionary biology, neurodynamics, swarm intelligence, and immune systems, this field develops algorithms that mimic natural processes such as natural selection, neural processing, genetic recombination, and collective behavior. These methods offer flexible and robust approaches to solving high-dimensional, non-linear, and dynamic problems often intractable by conventional techniques. Contributions to this Special Issue should include theoretical advances, algorithmic innovations, hybrid models, and practical applications across domains like robotics, pattern recognition, healthcare, and finance. Emphasis is placed on the integration of biologically plausible mechanisms with modern machine learning frameworks, including deep learning, reinforcement learning, and generative models. This convergence fosters the development of adaptive, explainable, and generalizable systems capable of learning from sparse or noisy data and evolving in changing environments. The Special Issue welcomes novel research and survey articles that contribute to the understanding, design, and application of bio-inspired and evolutionary learning techniques.

Dr. Dionisios N. Sotiropoulos
Guest Editor

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Keywords

  • bio-inspired algorithms
  • evolutionary computing
  • swarm intelligence
  • neural computation
  • genetic algorithms
  • machine learning
  • artificial immune systems
  • reinforcement learning
  • adaptation and self-organization
  • and hybrid intelligent systems

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Published Papers (1 paper)

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Review

35 pages, 7098 KB  
Review
Recent Advances in Optoelectronic Synaptic Devices for Neuromorphic Computing
by Heeseong Jang, Seohyeon Ju, Seeun Lee, Jaewoo Choi, Ungbin Byun, Kyeongjun Min, Maria Rasheed and Sungjun Kim
Biomimetics 2025, 10(9), 584; https://doi.org/10.3390/biomimetics10090584 - 3 Sep 2025
Abstract
We explore recent advancements in optoelectronic synaptic devices across four key aspects: mechanisms, materials, synaptic properties, and applications. First, we discuss fundamental working principles, including oxygen vacancy ionization, defect trapping, and heterojunction-based charge modulation, which contribute to synaptic plasticity. Next, we examine the [...] Read more.
We explore recent advancements in optoelectronic synaptic devices across four key aspects: mechanisms, materials, synaptic properties, and applications. First, we discuss fundamental working principles, including oxygen vacancy ionization, defect trapping, and heterojunction-based charge modulation, which contribute to synaptic plasticity. Next, we examine the role of 0D, 1D, and 2D materials in optimizing device performance, focusing on their unique electronic, optical, and mechanical properties. We then analyze synaptic properties such as excitatory post-synaptic current (EPSC), visual adaptation, transition from short-term to long-term plasticity (STP to LTP), nociceptor-inspired responses, and associative learning mechanisms. Finally, we highlight real-world applications, including artificial vision systems, reservoir computing for temporal data processing, adaptive neuromorphic computing for exoplanet detection, and colored image recognition. By consolidating recent developments, this paper provides insights into the potential of optoelectronic synaptic devices for next-generation computing architectures, bridging the gap between optics and neuromorphic engineering. Full article
(This article belongs to the Special Issue Bio-Inspired Machine Learning and Evolutionary Computing)
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