| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 361 | 59 | 63 | 16 |
The scientific interests tackled in Evolutionary Intelligence are Artificial intelligence, Algorithm, Machine learning, Pattern recognition and Artificial neural network. As a part of it, discussions in Artificial intelligence involve topics like Classifier (UML), Feature selection, Evolutionary algorithm, Support vector machine and Segmentation. Evolutionary Intelligence explores research in Evolutionary algorithm and the adjacent study of Evolutionary computation.
In addition to Algorithm research, Evolutionary Intelligence aims to explore topics under Genetic algorithm, Convergence (routing) and Benchmark (computing). The journal emphasizes research on Machine learning, which includes concerns such as Learning classifier system. The majority of Pattern recognition studies in it are focused on the subject of Feature extraction.
Studies on Optimization problem discussed in it link to the field of Metaheuristic.
The main points discussed in the most cited papers deal with Artificial intelligence, Machine learning, Evolutionary algorithm, Artificial neural network and Optimization problem. Artificial intelligence research in the most cited papers connects with the study of Pattern recognition. Issues in Machine learning were discussed in the most cited papers, taking into consideration concepts from other disciplines like Classifier (UML), Neuro-fuzzy and Data mining.
The topics of Artificial intelligence, Algorithm, Pattern recognition, Artificial neural network and Particle swarm optimization are the focal point of discussions in Evolutionary Intelligence. The research on Artificial intelligence discussed in it draws on the closely related field of Machine learning. The work on Algorithm tackled in it brings together disciplines like Convergence (routing), Chaotic, Cluster analysis and Benchmark (computing).
Cluster analysis research featured in it incorporates concerns from various other topics such as Data mining and Fuzzy logic. Some problems in Pattern recognition that were presented in Evolutionary Intelligence overlapped with concepts under Image (mathematics) and Thresholding. The Optimization problem works featured in it incorporate elements from Differential evolution 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 Evolutionary Intelligence (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 Intelligence (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, 12.29% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 20.77% were posted by at least one author from the top 10 institutions publishing in the journal. Another 8.70% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 13.53% of all publications and 57.00% 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.
For those who find these findings compelling and are interested in becoming a researcher in Evolutionary Intelligence, diverse academic and career paths can lead to this prestigious field. The foundation is often built on a strong understanding of mathematics, data analysis, algorithms, and machine learning principles. This can be achieved through a combination of formal education, hands-on experiences, and continuous learning. A potential path could start with a Bachelor's degree in Computer Science, Information Technology, Statistics, Mathematics, or related fields. During under-graduate studies, a focus on courses dealing with Artificial Intelligence, Machine Learning, Algorithms, Data Mining and their practical applications can provide essential skills and knowledge. Further enhancement in understanding can be achieved through a Master's degree or Doctorate in a related field, focusing on Evolutionary Intelligence. While formal education lays the groundwork, real-world experience is indispensable. This could come from internships, entry-level positions in AI or data analysis, or working on individual or open-source projects. Commitment to continuous learning through reading published research papers, attending seminars, workshops, and conferences can also help stay abreast of the latest advances in the field. Becoming a teacher in this field requires additional steps, focusing on pedagogical skills and potentially earning a teaching license. Understanding the steps involved, especially specific to the geographic area, can aid in this process. For instance, for those interested in academia in Massachusetts, guidance can be found by visiting how to become a teacher in Massachusetts. Finally, it's crucial to remember that while the objective may be to become a researcher in Evolutionary Intelligence, the path will be unique to each individual. Leveraging one's strengths and interests, being adaptive, and a continuous pursuit of knowledge are key ingredients to success in this exciting field.
G. Thippa Reddy;M. Praveen Kumar Reddy;Kuruva Lakshmanna;Dharmendra Singh Rajput
(2020)Tong Anh Tuan;Hoang Viet Long;Le Hoang Son;Raghvendra Kumar
(2020)Farhad Soleimanian Gharehchopogh;Isa Maleki;Zahra Asheghi Dizaji
(2021)Unknown
(2022)Ritam Guha;Manosij Ghosh;Souvik Kapri;Sushant Shaw
(2021)Pradeep Jangir;Hitarth Buch;Seyedali Mirjalili;Premkumar Manoharan
(2021)Narinder Singh;S. B. Singh;Essam H. Houssein
(2020)Ali Mohammadzadeh;Mohammad Masdari;Farhad Soleimanian Gharehchopogh;Ahmad Jafarian
(2021)Seifedine Kadry;V. Rajinikanth;N. Sri Madhava Raja;D. Jude Hemanth
(2021)Exploring online degrees in Computer Science can offer flexibility and accessibility for students at various stages of their educational journey. Many learners opt for a self paced online degree to balance studies with work or personal commitments. This approach allows students to progress at their own speed without compromising depth of learning.
For those seeking to advance their expertise, there are numerous affordable online masters programs that provide specialized knowledge in areas like artificial intelligence, cybersecurity, and software development. These programs combine cost-effectiveness with convenience, making graduate education more accessible.
Students new to the field often find starting with one of the associates degrees in computer science or related disciplines a practical choice. These degrees offer foundational skills and can lead to entry-level positions or transfer opportunities to bachelor's programs.
Selecting an institution is equally important. Candidates should consider enrolling in the best online colleges to ensure quality education and recognized credentials that support diverse career pathways in technology-focused roles.
