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Mathematics
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Advances on Evolutionary Computing
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Advances on Evolutionary Computing

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 2189

Special Issue Editor

Prof. Dr. Jiajun Zhou

E-Mail Website
Guest Editor
School of Computer science, China University of Geosciences, Wuhan 430072, China
Interests: computational intelligence; production scheduling; network cooperative manufacturing; swarm intelligence; evolutionary computing and its applications

Special Issue Information

Dear Colleagues,

Evolutionary computing has attracted the attention of researchers over the years due to its applicability and flexibility regarding handling constraints, computational expensive, dynamic changes, and multiple conflicting objectives. With the growth of complex applications and real-world engineering scenes, researchers in this field have to continuously face new challenges, which require new technologies and innovative solution methods. This Special Issue focuses on the research frontiers of evolutionary computing in both practical and theoretical aspects. Examples of new technologies include approaches that combine machine learning and evolutionary computation to solve difficult engineering problems. Examples of innovative solution methods include designing a new framework of evolutionary computing and rigorous analyses of related techniques. Other cutting-edge techniques to handle challenging issues in evolutionary computing are also highly encouraged. I sincerely invite you to submit your best research to this Special Issue on “Frontiers Related to Evolutionary Computing”.

Prof. Dr. Jiajun Zhou
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. 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

  • evolving deep learning
  • evolutionary multitasking optimization
  • surrogate-assisted evolutionary algorithms
  • transfer learning driven search
  • many-objective optimization
  • large scale optimization
  • dynamic optimization

Published Papers (1 paper)

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Research

14 pages, 1120 KiB  
Article
A General-Purpose Multi-Dimensional Convex Landscape Generator
by Wenwen Liu, Shiu Yin Yuen, Kwok Wai Chung and Chi Wan Sung
Mathematics 2022, 10(21), 3974; https://doi.org/10.3390/math10213974 - 26 Oct 2022
Cited by 2 | Viewed by 1840
Abstract
Heuristic and evolutionary algorithms are proposed to solve challenging real-world optimization problems. In the evolutionary community, many benchmark problems for empirical evaluations of algorithms have been proposed. One of the most important classes of test problems is the class of convex functions, particularly [...] Read more.
Heuristic and evolutionary algorithms are proposed to solve challenging real-world optimization problems. In the evolutionary community, many benchmark problems for empirical evaluations of algorithms have been proposed. One of the most important classes of test problems is the class of convex functions, particularly the d-dimensional sphere function. However, the convex function type is somewhat limited. In principle, one can select a set of convex basis functions and use operations that preserve convexity to generate a family of convex functions. This method will inevitably introduce bias in favor of the basis functions. In this paper, the problem is solved by employing insights from computational geometry, which gives the first-ever general-purpose multi-dimensional convex landscape generator. The new proposed generator has the advantage of being generic, which means that it has no bias toward a specific analytical function. A set of N random d-dimensional points is generated for the construction of a d-dimensional convex hull. The upper part of the convex hull is removed by considering the normal of the polygons. The remaining part defines a convex function. It is shown that the complexity of constructing the function is O(Md3), where M is the number of polygons of the convex function. For the method to work as a benchmark function, queries of an arbitrary (d−1) dimensional input are generated, and the generator has to return the value of the convex function. The complexity of answering the query is O(Md). The convexity of the function from the generator is verified with a nonconvex ratio test. The performance of the generator is also evaluated using the Broyden–Fletcher–Goldfarb–Shanno (BFGS) gradient descent algorithm. The source code of the generator is available. Full article
(This article belongs to the Special Issue Advances on Evolutionary Computing)
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