Biological Optimisation and Management
A section of Biomimetics (ISSN 2313-7673).
Section Information
Biologically inspired optimisation approaches use mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. For example, so-called evolutionary algorithms perform well at approximating solutions to all types of problems because they ideally do not make any assumption about the underlying fitness landscape. Simple evolutionary algorithms can solve often complex problems. Another approach of biological optimisation is computer aided optimization that is based on the simulation of the growth rule of biological systems (tree, bone, etc.) and is therefore a method from the field of biomimetics. Theory- and simulation-based original publications as well as novel methodology papers are welcomed for this section.
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Special Issues
Following special issues within this section are currently open for submissions:
- Bio-Inspired AI: When Generative AI and Biomimicry Overlap (Deadline: 28 February 2026)
- Evolutionary and Nature-Inspired AI: Bridging the Gap Between Engineering and Computing (Deadline: 30 April 2026)
- Application of Nature-Inspired Algorithms and Technologies in Engineering (Deadline: 25 May 2026)
- Computer-Aided Biomimetics: 3rd Edition (Deadline: 31 May 2026)
- Bio-Inspired Intelligent Computation for Optimisation in Business and Engineering Applications (Deadline: 15 June 2026)
- Advances in Digital Biomimetics (Deadline: 25 July 2026)
- Exploration of Bio-Inspired Computing: 3rd Edition (Deadline: 25 July 2026)
- Bioinspired Computational Intelligence and Optimization in Engineering Systems (Deadline: 31 July 2026)
- Bio-Inspired Computation and Its Applications (Deadline: 31 August 2026)
- Bio-Inspired Optimization Algorithms (Deadline: 30 September 2026)
- New Frontiers in Evolutionary Algorithms: Learning from Nature’s Optimization Strategies (Deadline: 20 October 2026)
- Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI (Deadline: 31 October 2026)