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Applied Sciences
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Bio-Inspired Evolutionary Computation (BI-EC) in Engineering Applications
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Bio-Inspired Evolutionary Computation (BI-EC) in Engineering Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 4133

Special Issue Editor

Prof. Dr. Alaa Sheta

E-Mail Website
Guest Editor
Computer Science Department, Southern Connecticut State University, 501 Crescent Str., New Haven, CT 06515, USA
Interests: evolutionary computation; image processing; robotics; neural networks; fuzzy logic; data mining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Bio-inspired evolutionary computation (EC) is a family of algorithms that was successfully used to solve the diversity of global optimization problems. EC was inspired by the notion of biological evolution. It is indicated as a subfield of soft computing techniques which include Neural Network and Fuzzy Logic. EC is a population-based problem solver. They can search for complex search spaces (i.e., non-convex/non-differentiable) and provide solutions that never been reported before. EC includes genetic algorithms (GAs), particle swarm optimization (PSO), Evolutionary Strategies (ES), genetic programming (GP), and many others. EC with its robust performance can solve various computer science and engineering applications not only in the analysis and design of systems but also in the quality enhancement of the final product. This will save time, effort and money. In engineering problems, it is necessary to deal with noisy data, process faults or uncertainty in calculations such as in the case of mechanical, chemical and industrial processes.

This special issue is dedicated to present the latest developments in the area of evolutionary computation and engineering applications. Our goal is to bring together researchers exploring techniques and applications in evolutionary computation theory and applications. Special interest will be towards, EC representations, search mechanisms, and industrial applications. Authors are invited to submit their original and unpublished work to this special session.

Topics of interest include

  • Genetic Algorithms
  • Genetic Programming
  • Evolutionary Strategies
  • Evolutionary Programming
  • Hybrid Computation
  • Optimization of Neural Networks
  • Cooperative Co-evolution
  • Particle Swarm Optimization
  • Differential Evolution;
  • Ant Colony Optimization;
  • Artificial Bee Colony;
  • Evolutionary Computation;
  • Hybrid Intelligent Systems;
  • Others

This includes engineering applications of EC methods and technologies such as:

  • Control and manufacturing applications
  • Mobile Robots
  • Medical Applications
  • Imaging and vision
  • Signal Processing
  • Time Tabling
  • Evolvable Hardware
  • Multi-Agent Systems
  • Communication systems
  • Computer Networks
  • Industrial Processes

Prof. Dr. Alaa Sheta
Guest Editor

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. Applied Sciences 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 2400 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.

Published Papers (2 papers)

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Research

15 pages, 955 KiB  
Article
Extended Evolutionary Algorithms with Stagnation-Based Extinction Protocol
by Gan Zhen Ye and Dae-Ki Kang
Appl. Sci. 2021, 11(8), 3461; https://0-doi-org.brum.beds.ac.uk/10.3390/app11083461 - 12 Apr 2021
Cited by 3 | Viewed by 1821
Abstract
Extinction has been frequently studied by evolutionary biologists and is shown to play a significant role in evolution. The genetic algorithm (GA), one of popular evolutionary algorithms, has been based on key concepts in natural evolution such as selection, crossover, and mutation. Although [...] Read more.
Extinction has been frequently studied by evolutionary biologists and is shown to play a significant role in evolution. The genetic algorithm (GA), one of popular evolutionary algorithms, has been based on key concepts in natural evolution such as selection, crossover, and mutation. Although GA has been widely studied and implemented in many fields, little work has been done to enhance the performance of GA through extinction. In this research, we propose stagnation-driven extinction protocol for genetic algorithm (SDEP-GA), a novel algorithm inspired by the extinction phenomenon in nature, to enhance the performance of classical GA. Experimental results on various benchmark test functions and their comparative analysis indicate the effectiveness of SDEP-GA in terms of avoiding stagnation in the evolution process. Full article
(This article belongs to the Special Issue Bio-Inspired Evolutionary Computation (BI-EC) in Engineering Applications)
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10 pages, 286 KiB  
Article
Improving Ant Collaborative Filtering on Sparsity via Dimension Reduction
by Xiaofeng Liao, Xiangjun Li, Qingyong Xu, Hu Wu and Yongji Wang
Appl. Sci. 2020, 10(20), 7245; https://0-doi-org.brum.beds.ac.uk/10.3390/app10207245 - 16 Oct 2020
Cited by 3 | Viewed by 1566
Abstract
Recommender systems should be able to handle highly sparse training data that continues to change over time. Among the many solutions, Ant Colony Optimization, as a kind of optimization algorithm modeled on the actions of an ant colony, enjoys the favorable characteristic of [...] Read more.
Recommender systems should be able to handle highly sparse training data that continues to change over time. Among the many solutions, Ant Colony Optimization, as a kind of optimization algorithm modeled on the actions of an ant colony, enjoys the favorable characteristic of being optimal, which has not been easily achieved by other kinds of algorithms. A recent work adopting genetic optimization proposes a collaborative filtering scheme: Ant Collaborative Filtering (ACF), which models the pheromone of ants for a recommender system in two ways: (1) use the pheromone exchange to model the ratings given by users with respect to items; (2) use the evaporation of existing pheromone to model the evolution of users’ preference change over time. This mechanism helps to identify the users and the items most related, even in the case of sparsity, and can capture the drift of user preferences over time. However, it reveals that many users share the same preference over items, which means it is not necessary to initialize each user with a unique type of pheromone, as was done with the ACF. Regarding the sparsity problem, this work takes one step further to improve the Ant Collaborative Filtering’s performance by adding a clustering step in the initialization phase to reduce the dimension of the rate matrix, which leads to the results that K<<#users, where K is the number of clusters, which stands for the maximum number of types of pheromone carried by all users. We call this revised version the Improved Ant Collaborative Filtering (IACF). Experiments are conducted on larger datasets, compared with the previous work, based on three typical recommender systems: (1) movie recommendations, (2) music recommendations, and (3) book recommendations. For movie recommendation, a larger dataset, MoviesLens 10M, was used, instead of MoviesLens 1M. For book recommendation and music recommendation, we used a new dataset that has a much larger size of samples from Douban and NetEase. The results illustrate that our IACF algorithm can better deal with practical recommendation scenarios that handle sparse dataset. Full article
(This article belongs to the Special Issue Bio-Inspired Evolutionary Computation (BI-EC) in Engineering Applications)
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