| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 38 | 164 | 536 | 67 |
The journal tackles a plethora of topics, such as Evolutionary algorithm, Mathematical optimization, Evolutionary computation, Artificial intelligence and Genetic algorithm. Algorithm, Algorithm design, Pareto principle, Set (abstract data type) and Selection (genetic algorithm) are some topics wherein Evolutionary algorithm research discussed in it have an impact. In addition to Mathematical optimization research, IEEE Transactions on Evolutionary Computation aims to explore topics under Convergence (routing) and Benchmark (computing).
The research on Evolutionary computation featured in it combines topics in other fields like Theoretical computer science and Differential evolution. It explores topics in Artificial intelligence which can be helpful for research in disciplines like Machine learning, Data mining and Pattern recognition. The work on Genetic algorithm tackled in it brings together disciplines like Local search (optimization), Crossover and Search algorithm.
Research on Metaheuristic addressed in the journal frequently intersections with the field of Multi-swarm optimization.
The journal articles generally zeroe in on subjects such as Mathematical optimization, Evolutionary algorithm, Evolutionary computation, Multi-objective optimization and Genetic algorithm. The journal papers explore issues in Mathematical optimization which can be linked to other research areas like Algorithm and Convergence (routing). Aside from discussions in Evolutionary algorithm, the most cited papers also deal with the subject of Metaheuristic which intersects with Combinatorial optimization disciplines.
The foci of the journal are Mathematical optimization, Evolutionary algorithm, Artificial intelligence, Multi-objective optimization and Optimization problem. Discussions in it are anchored in the field of Mathematical optimization but it branches out to cover the subject of interrelated disciplines, including
While work presented in the journal provided substantial information on Evolutionary computation, it also covered topics in Scheme (programming language) and Complex network. The journal tackles studies in Machine learning and the interrelated subject of Knowledge transfer and Task (computing) to gain insights into Artificial intelligence. Topics in Multi-objective optimization were tackled in line with various other fields like Test suite, Pareto principle and Scale (descriptive set theory).
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in IEEE Transactions on Evolutionary Computation (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in IEEE Transactions on Evolutionary Computation (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 6.67% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 57.94% were posted by at least one author from the top 10 institutions publishing in the journal. Another 8.73% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 11.90% of all publications and 21.43% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
While the article has provided a comprehensive overview of the various topics and research areas discussed in the 'IEEE Transactions on Evolutionary Computation' journal, it lacks a section that deals with the potential use cases and future perspectives of the discussed research. This kind of information would carry much interest for a variety of audiences, including but not limited to, students interested in the field, researchers seeking potential activities for future work, and practitioners looking for the latest advancements and their practical implications. The cutting-edge research published in the 'IEEE Transactions on Evolutionary Computation' journal finds varied applications across different sectors. For instance, the areas of Evolutionary Algorithm and Artificial Intelligence have numerous potential uses. They can be applied to effectively solve complex problems in diverse domains ranging from engineering and health care to finance and logistics. Moreover, their influence is also being increasingly recognized in education. For instance, for those interested in becoming a special education teacher in Oregon, understanding and applying these concepts can be greatly beneficial. Learn more about it here.
Looking towards the future, Evolutionary Computation and similarly related research fields carry immense potential. The exponential increase in computing power and data availability will likely catapult these technologies to the forefront of scientific and industrial innovation. Consequently, these research areas can be expected to have profound impacts in shaping the landscape of emerging disciplines like quantum computing, robotics, and smart cities.
Despite varied challenges and complex issues, the commitment of researchers, as presented in the papers of 'IEEE Transactions on Evolutionary Computation', gives reason for optimism about the future. The coming years will undeniably unfold exciting developments and breakthroughs in this prominent field of study.
Yanan Sun;Bing Xue;Mengjie Zhang;Gary G. Yen
(2020)Ye Tian;Tao Zhang;Jianhua Xiao;Xingyi Zhang
(2021)Unknown
(2023)Kavitesh Kumar Bali;Yew-Soon Ong;Abhishek Gupta;Puay Siew Tan
(2020)Ye Tian;Xingyi Zhang;Chao Wang;Yaochu Jin
(2020)Unknown
(2022)Yanan Sun;Handing Wang;Bing Xue;Yaochu Jin
(2020)Changwu Huang;Yuanxiang Li;Xin Yao
(2020)Zi-Jia Wang;Zhi-Hui Zhan;Ying Lin;Wei-Jie Yu
(2020)Ke Shang;Hisao Ishibuchi;Linjun He;Lie Meng Pang
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