1063-6560
Published by: Massachusetts Institute of Technology Press
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 326 | 52 | 58 | 18 |
Evolutionary Computation facilitates discussions on Mathematical optimization, Evolutionary algorithm, Artificial intelligence, Machine learning and Algorithm. It tackles studies in Selection (genetic algorithm) and the interrelated subject of Fitness proportionate selection to gain insights into Mathematical optimization. It focuses on Evolutionary algorithm but the discussions also offer insight into other areas such as Evolutionary computation, Function (mathematics), Set (abstract data type) and Theoretical computer science.
The in-depth study on Artificial intelligence also explores topics in the intersecting field of Pattern recognition. Evolutionary Computation features studies on Machine learning, including topics such as Fitness function. The study on Algorithm presented in the journal intersects with subjects under the field of Crossover.
The Genetic algorithm research dealing mostly with Genetic representation is the focus of Evolutionary Computation. More specifically, the research on Local search (optimization) in the journal is related to Memetic algorithm. The work on Multi-objective optimization addressed in it expands to the thematically related Pareto principle.
The most cited articles primarily focus on research topics in Mathematical optimization, Evolutionary algorithm, Artificial intelligence, Machine learning and Multi-objective optimization. The works on Evolutionary algorithm tackled in the journal papers bring together disciplines like Evolutionary computation, Algorithm and Set (abstract data type). The most cited publications explore topics in Multi-objective optimization which can be helpful for research in disciplines like Sorting, Pareto principle and Test functions for optimization.
The journal investigates areas of study like Evolutionary algorithm, Mathematical optimization, Artificial intelligence, Genetic programming and Symbolic regression. Topics in Evolutionary algorithm explored in the journal were investigated in conjunction with research in Set (abstract data type), Regular polygon, Function (mathematics), Optimization algorithm and Computation. The research on Mathematical optimization tackled can also make contributions to studies in the areas of Multiplicative function, Hitting time and Selection (genetic algorithm).
Evolutionary Computation addresses concerns in Artificial intelligence which are intertwined with other disciplines, such as Field (computer science) and Machine learning. The concepts on Genetic programming presented in it can also apply to other research fields, including Routing (electronic design automation), Process (engineering) and Test set. The featured Symbolic regression studies mainly concentrate on Theoretical computer science but also cover areas of interest in Interpretability, Linkage (software), Cartesian genetic programming and Population diversity.
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 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 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, 0.00% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 8.70% were posted by at least one author from the top 10 institutions publishing in the journal. Another 26.09% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 21.74% of all publications and 43.48% 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.
Numerous opportunities are available for those interested in building a career in the field of Evolutionary Computation. With industries heavily reliant on advanced computation methods, possessing strong skills, particularly in evolutionary algorithms and artificial intelligence, is highly sought after by recruiters. Interestingly, while computational research might seem far removed from traditional classrooms, the underlying principles also play an essential role in the field of education. For instance, a middle school math teacher needs to effectively explain mathematical optimization, fundamental to Computational Evolution. If you are considering a career as a middle school math teacher in Massachusetts, you might want to know how long does it take to become a middle school math teacher in Massachusetts. Moreover, according to our research, the most competent researchers in this field have often published works on evolutionary algorithms and artificial intelligence, with their studies finding application in areas as diverse as machine learning and multi-objective optimization. Therefore a solid knowledge base in these areas can serve as a foundation for a thriving career in evolutionary computation. Finally, remember that continuous learning and adaptation are key to any successful career. Whether you're an aspiring middle school math teacher or a future leader in computational research, a commitment to learning and enhancing your knowledge of mathematical optimization, algorithms, and artificial intelligence will undoubtedly position you for success in any chosen career path.
Marco Virgolin;Tanja Alderliesten;Cees Witteveen;Peter A. N. Bosman
(2021)Miqing Li;Xin Yao
(2020)Wanru Gao;Samadhi Nallaperuma;Frank Neumann
(2021)Mario A Muñoz;Kate Smith-Miles
(2020)Yuxin Liu;Yi Mei;Mengjie Zhang;Zili Zhang
(2020)Jordan MacLachlan;Yide Mei;Jürgen Branke;Mengjie Zhang
(2020)Binzi Xu;Yi Mei;Yan Wang;Zhicheng Ji
(2021)Leonardo C. T. Bezerra;Manuel López-Ibáñez;Thomas Stützle
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