2210-6502
Published by: Elsevier
https://www.journals.elsevier.com/swarm-and-evolutionary-computation/
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 75 | 179 | 379 | 49 |
The aim of the journal is to expand the discussion of research in Mathematical optimization, Evolutionary algorithm, Optimization problem, Algorithm and Benchmark (computing). Most of the works presented in the journal deals with Mathematical optimization but it intersects with the subject of Convergence (routing). Swarm and evolutionary computation addresses concerns in Evolutionary algorithm which are intertwined with other disciplines, such as Selection (genetic algorithm), Set (abstract data type) and Differential evolution.
The journal focused on Differential evolution research but expanded to cover Mutation (genetic algorithm). The work on Optimization problem tackled in the journal brings together disciplines like Optimization algorithm and Cluster analysis. Heuristic (computer science) is a focus of the presented Algorithm works and it dives deep in Heuristic (computer science).
The Benchmark (computing) study featured in Swarm and evolutionary computation draws parallels with the field of Local search (optimization). The research on Artificial intelligence featured in the journal combines topics in other fields like Genetic algorithm, Data mining and Pattern recognition. As a part of Swarm and evolutionary computation, discussions in Particle swarm optimization involve topics like Multi-swarm optimization and Swarm intelligence.
The journal publications are organized to reinforce research efforts on Mathematical optimization, Artificial intelligence, Optimization problem, Evolutionary algorithm and Particle swarm optimization. While Mathematical optimization is the focus of the journal articles, it also provides insights into the studies of Algorithm and Benchmark (computing). While the journal publications focused on Artificial intelligence, they were also able to explore topics like Swarm intelligence, Machine learning, Data mining and Pattern recognition.
Swarm and evolutionary computation focuses on Mathematical optimization, Evolutionary algorithm, Optimization problem, Benchmark (computing) and Algorithm. Issues in Mathematical optimization were discussed, taking into consideration concepts from other disciplines like Convergence (routing), Set (abstract data type) and Job shop scheduling. Evolutionary algorithm research discussed in it aim to provide more information in the subject of Artificial intelligence.
The journal holds forums on Artificial intelligence that merges themes from other disciplines such as Machine learning and Pattern recognition. Topics in Optimization problem explored in Swarm and evolutionary computation were investigated in conjunction with research in Field (computer science), Optimization algorithm, Cluster analysis, Swarm intelligence and Evolution strategy. Benchmark (computing) research featured in the journal incorporates concerns from various other topics such as Metaheuristic, Local search (optimization), Selection (genetic algorithm), Decomposition (computer science) and Ranking.
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 Swarm and 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 Swarm and 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, 4.24% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 25.32% were posted by at least one author from the top 10 institutions publishing in the journal. Another 12.66% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 13.29% of all publications and 48.73% 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 embarking on a career in Swarm and evolutionary computation, one might also consider the avenue of teaching. Private school teaching, for instance, could be an enticing prospect for those with a passion for imparting knowledge. In particular, Arkansas has a diverse range of private educational institutions that might be of interest. However, some may ponder, "Do private school teachers need a degree in Arkansas?" The answer may not be as straightforward as one thinks. While a degree in education can be helpful, many private schools are increasingly open to individuals who have substantial knowledge and experience in a particular area, like Swarm and evolutionary computation. That being said, those with a bachelor's degree related to the subject they wish to teach, coupled with teaching experience or a teaching license, might have a better chance at securing a position at a private school. Becoming a teacher in a private school, including those in Arkansas, requires passion, dedication, and subject matter expertise. Regardless of degree requirements, aspiring teachers must also continue honing their skills and engaging in lifelong learning to keep their knowledge updated and relevant. This way, they can provide quality education and positively impact their students' lives. So, if you are considering a career makeover, private school teaching in Arkansas could be an avenue worth considering.
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(2021)Pursuing a degree in Computer Science in the USA opens the door to some of the most lucrative careers in technology and innovation. For those looking to maximize earning potential, exploring most lucrative degrees can provide valuable insight into how a Computer Science degree stacks up in the job market.
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