| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 502 | 21 | 44 | 12 |
Genetic Programming and Evolvable Machines mainly tackles studies in Genetic programming, Artificial intelligence, Evolutionary algorithm, Machine learning and Theoretical computer science. The Genetic programming works featured in the journal incorporate elements from Algorithm, Field (computer science) and Crossover. Most of the works presented in it deals with Algorithm but it intersects with the subject of Genetic algorithm.
The work on Artificial intelligence addressed in the journal expands to the thematically related Set (abstract data type). The Evolutionary algorithm study presented in it encompasses related topics like Evolutionary programming and also examines its connection to subjects such as Context (language use). The journal features studies on Evolutionary programming, including topics such as Interactive evolutionary computation.
The journal papers are organized to address concerns in the fields of Genetic programming, Artificial intelligence, Algorithm, Genetic representation and Evolutionary computation. Issues in Genetic programming were discussed in the journal papers, taking into consideration concepts from other disciplines like Theoretical computer science, Data mining, Selection (genetic algorithm) and Crossover. The journal articles investigate Artificial intelligence research which frequently intersects with Machine learning.
The journal focuses largely on the fields of Genetic programming, Artificial intelligence, Machine learning, Evolutionary algorithm and Theoretical computer science. The work on Genetic programming presented in Genetic Programming and Evolvable Machines focuses on Symbolic regression in particular. Genetic Programming and Evolvable Machines explores topics in Artificial intelligence which can be helpful for research in disciplines like Use case and Modularity (biology).
It facilitates discussions in Selection method as part of the larger field of Machine learning, however, it also tackles fields such as Generality. Optimization problem, Grammatical evolution and Representation (mathematics) are some topics wherein Evolutionary algorithm research discussed in the journal have an impact. Some problems in Theoretical computer science that were presented in the journal overlapped with concepts under Disjoint sets and Regular expression.
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 Genetic Programming and Evolvable Machines (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 Genetic Programming and Evolvable Machines (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, 13.79% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 20.00% were posted by at least one author from the top 10 institutions publishing in the journal. Another 16.00% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 24.00% of all publications and 40.00% 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 genetic programming and evolvable machines field is a lucrative area of study with plenty of research opportunities, it might be worth considering how it translates into employment prospects for students seeking to make their mark in the field. The scope of these subjects isn't limited to research and development in IT or software sectors; it could also lead to unique career paths like becoming an art teacher, where you could use genetic algorithms to create innovative art pieces.
To become an art teacher who incorporates elements from genetic programming, artificial intelligence, and machine learning into lessons, certain prerequisites must be met. These requirements vary from state to state. For instance, in Massachusetts, certain state-specific {anchor} (art teacher requirements in Massachusetts) should be fulfilled. Qualified art teachers understanding genetic programming would be well-placed to inspire students to merge art and science, foster creativity, and develop novel approaches to problem-solving.
In addition to educational qualifications, a comprehensive understanding of genetic programming and evolvable machines is essential for such roles. Understanding key topics like evolutionary algorithm, machine learning, and artificial intelligence can contribute to a successful career in this area. Future professionals in this field have the potential to push the boundaries of traditional disciplines, marrying creativity and technology in unique and exciting ways.
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