| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 396 | 49 | 55 | 15 |
The journal investigates studies in Mathematical optimization, Artificial intelligence, Complex system, Evolutionary algorithm and Memetic algorithm. Memetic Computing connects research in Mathematical optimization with the related topic of Benchmark (computing). Concepts in Data mining, as well as related topics in Feature selection, are covered in the Artificial intelligence research presented in the journal.
While Memetic Computing focused on Evolutionary algorithm, it was also able to explore topics like Evolutionary computation, Convergence (routing), Theoretical computer science and Set (abstract data type). Aside from Memetic algorithm, the journal also covered works in the field of Job shop scheduling. The journal explores research in Genetic algorithm alongside concepts in Particle swarm optimization and other areas of study in Simulated annealing.
The research on Optimization problem featured in it combines topics in other fields like Multi-objective optimization and Differential evolution. As a part of the journal, discussions in Machine learning involve topics like Support vector machine and Artificial neural network. The study on Local search (optimization) presented in it intersects with subjects under the field of Crossover.
The journal papers are organized to reinforce research efforts on Mathematical optimization, Artificial intelligence, Local search (optimization), Complex system and Evolutionary algorithm. Mathematical optimization study tackled in the published articles is connected to the field of Benchmark (computing). The most cited articles focus on Artificial intelligence but the discussions also offer insight into other areas such as Swarm intelligence, Machine learning, Field (computer science) and Pattern recognition.
The main research concerns discussed in Memetic Computing are Mathematical optimization, Complex system, Evolutionary algorithm, Optimization problem and Benchmark (computing). The journal dives deep in exploring the relationship between the study of Mathematical optimization and Convergence (routing). The journal facilitates discussions on Complex system that incorporate concepts from other fields like Memetic algorithm, Monotonic function and Test set.
Evolutionary algorithm research presented in Memetic Computing encompasses a variety of subjects, including Automation, Construct (python library) and Field (computer science). Distributed computing and Task (computing) are some topics wherein Optimization problem research discussed in it have an impact. Memetic Computing addresses concerns in Local search (optimization) which are intertwined with other disciplines, such as Set (abstract data type) and Metaheuristic.
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 Memetic Computing (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 Memetic Computing (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, 0.00% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 24.14% were posted by at least one author from the top 10 institutions publishing in the journal. Another 24.14% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 17.24% of all publications and 34.48% 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.
A career in research and academia may be appealing to those interested in the concept of Memetic Computing. There are various paths within the field, including a research scientist, post-doctoral researcher, professor, etc. The career prospects may vary depending on the individual's academic qualifications and interests. For aspiring researchers, it is important to note that a high level of theoretical understanding as well as practical experience is often crucial in this field. Depending on the role one is interested in, the required qualifications may vary. For instance, university-level teaching roles typically require a PhD and a strong publication record. One specific career path that is gaining interest is private school teaching, especially within subjects such as applied mathematics and computer science. These teachers often have the opportunity to contribute significantly in student's understanding and application of topics relating to Memetic Computing, thereby developing the next generation of researchers in the field. If you're interested in becoming a private school teacher, you may want to consider the relevant qualifications and steps required. The link do private school teachers need a degree in new hampshire provides useful insights into this process for those based in New Hampshire. Remember, whether your passion in Memetic Computing lies in academia, research, or teaching, there are many valuable roles you can play in this fascinating field to bring advancements and contribute to its growth.
Haoran Li;Fazhi He;Yilin Chen;Yiteng Pan
(2021)Chao Lu;Liang Gao;Xinyu Li;Chengyu Hu
(2020)Lin Yang;Shangce Gao;Haichuan Yang;Zonghui Cai
(2021)Liangjie Zhang;Yuling Xie;Jianjun Chen;Liang Feng
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