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Mathematics
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Biologically Inspired Computing, 2nd Edition
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Biologically Inspired Computing, 2nd Edition

A special issue of Mathematics (ISSN 2227-7390).

Deadline for manuscript submissions: 20 May 2024 | Viewed by 1371

Special Issue Editors

Prof. Dr. Thomas Hanne

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Guest Editor
Institute for Information Systems, University of Applied Sciences and Arts Northwestern Switzerland, 4142 Münchenstein, Switzerland
Interests: computational intelligence; optimization simulation; multicriteria decision analysis; logistics and supply chain management; evolutionary computation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhihua Cui

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Guest Editor
Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan 030024, Shanxi, China
Interests: cloud computing; network security; big data modeling and optimization
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Dr. Gai-Ge Wang

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Guest Editor
Department of Computer Science and Technology, Ocean University of China, Qingdao 266100, China
Interests: swarm intelligence; evolutionary computation; big data optimization
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Prof. Dr. Linqiang Pan

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Guest Editor
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China
Interests: biocomputing; artificial intelligence
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Dr. Harish Garg

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Guest Editor
School of Mathematics, Thapar Institute of Engineering & Technology, Patiala 147004, Punjab, India
Interests: multicriteria decision making; decision support systems; metaheuristic-based optimization; soft computing; reliability and risk analysis; rough set theory; hesitant set; soft set theory
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

BIC, short for biologically inspired computing, is a field of study that loosely combines the related subfields of connectionism, social behavior, and emergence. It is often closely related to the field of artificial intelligence, as many of its goals can be linked to machine learning. It is also closely related to the fields of biology, computer science and mathematics. In short, it is the use of computers to simulate the phenomena of life and to improve the use of computers by studying living things. Biologically inspired computing is a major subset of natural computing. Biologically inspired computing is different from traditional artificial intelligence (AI) in that it uses a more evolved learning method instead of the so-called "creation theory" method in traditional artificial intelligence.

The purpose of this Special Issue is to gather a collection of articles that cover the latest developments in different fields of biologically inspired computing, evolutionary algorithms, biodegradability prediction, cellular automaton, the neural network, and others.

Prof. Dr. Thomas Hanne
Prof. Dr. Zhihua Cui
Dr. Gai-Ge Wang
Prof. Dr. Linqiang Pan
Dr. Harish Garg
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • biologically inspired computing
  • evolutionary algorithms
  • biodegradability prediction
  • cellular automaton
  • the neural network
  • artificial immune system

Published Papers (1 paper)

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Research

25 pages, 2257 KiB  
Article
A New Parallel Cuckoo Flower Search Algorithm for Training Multi-Layer Perceptron
by Rohit Salgotra, Nitin Mittal and Vikas Mittal
Mathematics 2023, 11(14), 3080; https://doi.org/10.3390/math11143080 - 12 Jul 2023
Cited by 3 | Viewed by 788
Abstract
This paper introduces a parallel meta-heuristic algorithm called Cuckoo Flower Search (CFS). This algorithm combines the Flower Pollination Algorithm (FPA) and Cuckoo Search (CS) to train Multi-Layer Perceptron (MLP) models. The algorithm is evaluated on standard benchmark problems and its competitiveness is demonstrated [...] Read more.
This paper introduces a parallel meta-heuristic algorithm called Cuckoo Flower Search (CFS). This algorithm combines the Flower Pollination Algorithm (FPA) and Cuckoo Search (CS) to train Multi-Layer Perceptron (MLP) models. The algorithm is evaluated on standard benchmark problems and its competitiveness is demonstrated against other state-of-the-art algorithms. Multiple datasets are utilized to assess the performance of CFS for MLP training. The experimental results are compared with various algorithms such as Genetic Algorithm (GA), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Evolutionary Search (ES), Ant Colony Optimization (ACO), and Population-based Incremental Learning (PBIL). Statistical tests are conducted to validate the superiority of the CFS algorithm in finding global optimum solutions. The results indicate that CFS achieves significantly better outcomes with a higher convergence rate when compared to the other algorithms tested. This highlights the effectiveness of CFS in solving MLP optimization problems and its potential as a competitive algorithm in the field. Full article
(This article belongs to the Special Issue Biologically Inspired Computing, 2nd Edition)
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