Journal of
Systemics, Cybernetics and Informatics
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ISSN: 1690-4524 (Online)


Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.

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Published by
The International Institute of Informatics and Cybernetics


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Academia.edu
(A Community of about 40.000.000 Academics)


Honorary Editorial Advisory Board's Chair
William Lesso (1931-2015)

Editor-in-Chief
Nagib C. Callaos


Sponsored by
The International Institute of
Informatics and Systemics

www.iiis.org
 

Editorial Advisory Board

Quality Assurance

Editors

Journal's Reviewers
Call for Special Articles
 

Description and Aims

Submission of Articles

Areas and Subareas

Information to Contributors

Editorial Peer Review Methodology

Integrating Reviewing Processes


Transdisciplinary Communication as a Meta-Framework of Digital Education
Rusudan Makhachashvili, Ivan Semenist
(pages: 1-6)

Multidisciplinary Learning Using Online Networking in Biomedical Engineering
Shigehiro Hashimoto
(pages: 7-12)

Augmented Intelligence for Advancing Healthcare
Mohammad Ilyas
(pages: 13-19)

A Transdisciplinary Approach to Refereeal
Russell Jay Hendel
(pages: 20-25)

The Impact of Convictions on Interlocking Systems
Teresa Henkle Langness
(pages: 26-33)

Collaborative Convergence: Finding the Language for Trans-Disciplinary Communication to Occur
Cristo Leon, James Lipuma
(pages: 34-37)

Bridging the Gap Between the World of Education and the World of Business via Standards to Develop Competences of the Future at Universities
Paweł Poszytek
(pages: 38-42)

Multidisciplinary Learning for Multifaceted Thinking in Globalized Society
Shigehiro Hashimoto
(pages: 43-48)

From Spirituality to Technontology in Education
Florent Pasquier
(pages: 49-52)

Differentiated Learning and Digital Game Based Learning: The KIDEDU Project
Eleni Tsami
(pages: 53-57)

Emerging Role of Artificial Intelligence
Mohammad Ilyas
(pages: 58-65)

Practicing Transdisciplinarity and Trans-Domain Approaches in Education: Theory of and Communication in Values and Knowledge Education (VaKE)
Jean-Luc Patry
(pages: 66-71)

Reflexive Practice for Inter and Trans Disciplinary Research in the Third Millennium
Maria Grazia Albanesi
(pages: 72-76)


 

Abstracts

 


ABSTRACT


Efficient Local Search in Imaging Optimization Problems with a Hybrid Evolutionary Algorithm

Igor Maslov


The paper focuses on the efficiency of local search in a Hybrid evolutionary algorithm (HEA), with application to optimization problem frequently encountered in electronic imaging. Although HEA can significantly improve the overall performance of evolutionary search, the direct usage of methods of local optimization gives rise to a few performance problems including a noticeable additional cost of fitness evaluations attributed to local search; an excessive waste of computational resources on a particular chromosome that is later discarded by the global search; and a possible convergence to a sub-optimal solution when the actual distance from the global optimum is not sufficiently small for the local search to successfully descend to the minimum point. Computational performance of local search can be potentially improved by applying the following techniques: using direct search that can better accommodate shape irregularities of fitness function; adding randomness and periodically re-positioning the search, thus preventing it from converging to a sub-optimal point; creating a tree-like structure for each local neighborhood that keeps track of the explored search space; using cyclic vs. complete local search, thus cutting down the excessive cost attributed to discarded chromosomes; incorporating image response analysis and providing the algorithm with a means of deriving problem-specific knowledge that speeds up the solution. A two-phase cyclic local search is proposed that incorporates these techniques. A series of computational experiments with 2-dimensional grayscale images provide experimental support for the proposed approach and show that computational performance of local search in imaging optimization with HEA can be significantly improved.

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