Charles Newton

1.2k total citations
13 papers, 796 citations indexed

About

Charles Newton is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Control and Systems Engineering. According to data from OpenAlex, Charles Newton has authored 13 papers receiving a total of 796 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 5 papers in Computational Theory and Mathematics and 4 papers in Control and Systems Engineering. Recurrent topics in Charles Newton's work include Metaheuristic Optimization Algorithms Research (6 papers), Advanced Multi-Objective Optimization Algorithms (5 papers) and Optimization and Mathematical Programming (4 papers). Charles Newton is often cited by papers focused on Metaheuristic Optimization Algorithms Research (6 papers), Advanced Multi-Objective Optimization Algorithms (5 papers) and Optimization and Mathematical Programming (4 papers). Charles Newton collaborates with scholars based in Australia, United Kingdom and Iceland. Charles Newton's co-authors include Ruhul Sarker, Hussein A. Abbass, Xin Yao, Ko-Hsin Liang, Joarder Kamruzzaman, David Hoffman, Thomas Philip Rúnarsson and John S. Singleton and has published in prestigious journals such as Computers & Operations Research, Computers & Industrial Engineering and Applied Intelligence.

In The Last Decade

Charles Newton

13 papers receiving 743 citations

Peers

Charles Newton

AI

CTM

IME

CSE

EEE

Sunith Bandaru Sweden

E.F. Khor Singapore

Andreas Beham Austria

Jianping Luo China

Tatsuya Okabe Germany

Susana Cecilia Esquivel Argentina

T.H. Lee Singapore

Gaoji Sun China

Pilar M. Ortigosa Spain

Sunith Bandaru Sweden

Charles Newton

335 ×0.8

AI

360 ×1.1

CTM

181 ×1.2

IME

51 ×0.6

CSE

41 ×0.5

EEE

Citations per year, relative to Charles Newton Charles Newton (= 1×) peers Sunith Bandaru

Countries citing papers authored by Charles Newton

Since Specialization

Citations

This map shows the geographic impact of Charles Newton's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Charles Newton with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Charles Newton more than expected).

Fields of papers citing papers by Charles Newton

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Charles Newton. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Charles Newton. The network helps show where Charles Newton may publish in the future.

Co-authorship network of co-authors of Charles Newton

This figure shows the co-authorship network connecting the top 25 collaborators of Charles Newton. A scholar is included among the top collaborators of Charles Newton based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Charles Newton. Charles Newton is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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13 of 13 papers shown

1.

Newton, Charles, et al.. (2021). Scalability in modeling and simulation systems for multi-agent, AI, and machine learning applications. 70–70. 3 indexed citations

2.

Sarker, Ruhul & Charles Newton. (2008). Optimization Modelling: A Practical Approach. TU Digital Collections (Thammasat University). 40 indexed citations

3.

Sarker, Ruhul & Charles Newton. (2007). Optimization Modelling. 23 indexed citations

4.

Sarker, Ruhul, Joarder Kamruzzaman, & Charles Newton. (2003). EVOLUTIONARY OPTIMIZATION (EvOpt): A BRIEF REVIEW AND ANALYSIS. International Journal of Computational Intelligence and Applications. 3(4). 311–330. 50 indexed citations

5.

Liang, Ko-Hsin, Xin Yao, & Charles Newton. (2003). Combining landscape approximation and local search in global optimization. 1514–1520. 28 indexed citations

6.

Abbass, Hussein A., Ruhul Sarker, & Charles Newton. (2002). Data Mining. IGI Global eBooks. 89 indexed citations

7.

Liang, Ko-Hsin, Xin Yao, & Charles Newton. (2002). Lamarckian evolution in global optimization. 4. 2975–2980. 7 indexed citations

8.

Abbass, Hussein A., Ruhul Sarker, & Charles Newton. (2002). PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems. DSPACE System - Metalibrary (University of the Coast). 2. 971–978. 395 indexed citations

9.

Liang, Ko-Hsin, Xin Yao, Charles Newton, & David Hoffman. (2002). A new evolutionary approach to cutting stock problems with and without contiguity. Computers & Operations Research. 29(12). 1641–1659. 64 indexed citations

10.

Sarker, Ruhul & Charles Newton. (2002). A genetic algorithm for solving economic lot size scheduling problem. Computers & Industrial Engineering. 42(2-4). 189–198. 51 indexed citations

11.

Sarker, Ruhul, Thomas Philip Rúnarsson, & Charles Newton. (2001). Genetic Algorithms for Solving a Class of Constrained Nonlinear Integer Programs. International Transactions in Operational Research. 8(1). 61–74. 9 indexed citations

12.

Sarker, Ruhul, Thomas Philip Rúnarsson, & Charles Newton. (2001). A Constrained Multiple Raw Materials Manufacturing Batch Sizing Problem. International Transactions in Operational Research. 8(2). 121–138. 7 indexed citations

13.

Liang, Ko-Hsin, Xin Yao, & Charles Newton. (2001). Adapting Self-Adaptive Parameters in Evolutionary Algorithms. Applied Intelligence. 15(3). 171–180. 30 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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