Example of IEEE Transactions on Evolutionary Computation format
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Example of IEEE Transactions on Evolutionary Computation format Example of IEEE Transactions on Evolutionary Computation format Example of IEEE Transactions on Evolutionary Computation format Example of IEEE Transactions on Evolutionary Computation format Example of IEEE Transactions on Evolutionary Computation format Example of IEEE Transactions on Evolutionary Computation format Example of IEEE Transactions on Evolutionary Computation format Example of IEEE Transactions on Evolutionary Computation format Example of IEEE Transactions on Evolutionary Computation format Example of IEEE Transactions on Evolutionary Computation format Example of IEEE Transactions on Evolutionary Computation format
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Example of IEEE Transactions on Evolutionary Computation format Example of IEEE Transactions on Evolutionary Computation format Example of IEEE Transactions on Evolutionary Computation format Example of IEEE Transactions on Evolutionary Computation format Example of IEEE Transactions on Evolutionary Computation format Example of IEEE Transactions on Evolutionary Computation format Example of IEEE Transactions on Evolutionary Computation format Example of IEEE Transactions on Evolutionary Computation format Example of IEEE Transactions on Evolutionary Computation format Example of IEEE Transactions on Evolutionary Computation format Example of IEEE Transactions on Evolutionary Computation format
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IEEE Transactions on Evolutionary Computation — Template for authors

Publisher: IEEE
Categories Rank Trend in last 3 yrs
Computational Theory and Mathematics #2 of 133 -
Software #8 of 389 down down by 5 ranks
Theoretical Computer Science #4 of 120 down down by 3 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 284 Published Papers | 6198 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 01/07/2020
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Related Journals

open access Open Access

IEEE

Quality:  
High
CiteRatio: 6.9
SJR: 0.679
SNIP: 1.934
open access Open Access
recommended Recommended

Springer

Quality:  
High
CiteRatio: 7.3
SJR: 0.73
SNIP: 1.279
open access Open Access

Cambridge University Press

Quality:  
Good
CiteRatio: 3.5
SJR: 0.685
SNIP: 1.383
open access Open Access

Springer

Quality:  
Good
CiteRatio: 3.8
SJR: 0.373
SNIP: 0.98

Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

11.169

31% from 2018

Impact factor for IEEE Transactions on Evolutionary Computation from 2016 - 2019
Year Value
2019 11.169
2018 8.508
2017 8.124
2016 10.629
graph view Graph view
table view Table view

21.8

4% from 2019

CiteRatio for IEEE Transactions on Evolutionary Computation from 2016 - 2020
Year Value
2020 21.8
2019 21.0
2018 19.1
2017 20.1
2016 16.2
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 31% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 4% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

3.463

13% from 2019

SJR for IEEE Transactions on Evolutionary Computation from 2016 - 2020
Year Value
2020 3.463
2019 3.993
2018 2.968
2017 3.493
2016 3.321
graph view Graph view
table view Table view

5.208

3% from 2019

SNIP for IEEE Transactions on Evolutionary Computation from 2016 - 2020
Year Value
2020 5.208
2019 5.365
2018 4.944
2017 4.947
2016 5.25
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 13% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 3% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

IEEE Transactions on Evolutionary Computation

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IEEE

IEEE Transactions on Evolutionary Computation

Papers on application, design, and theory of evolutionary computation, with emphasis given to engineering systems and scientific applications. Evolutionary optimization, machine learning, intelligent systems design, image processing and machine vision, pattern recognition, evo...... Read More

Computer Science

i
Last updated on
01 Jul 2020
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ISSN
1089-778X
i
Impact Factor
Maximum - 5.698
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
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Bibliography Name
IEEEtran
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Citation Type
Numbered
[25]
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Bibliography Example
C. W. J. Beenakker, “Specular andreev reflection in graphene,” Phys. Rev. Lett., vol. 97, no. 6, p.

Top papers written in this journal

Journal Article DOI: 10.1109/4235.996017
A fast and elitist multiobjective genetic algorithm: NSGA-II
Kalyanmoy Deb1, Amrit Pratap1, Sameer Agarwal1, T. Meyarivan1

Abstract:

Multi-objective evolutionary algorithms (MOEAs) that use non-dominated sorting and sharing have been criticized mainly for: (1) their O(MN/sup 3/) computational complexity (where M is the number of objectives and N is the population size); (2) their non-elitism approach; and (3) the need to specify a sharing parameter. In thi... Multi-objective evolutionary algorithms (MOEAs) that use non-dominated sorting and sharing have been criticized mainly for: (1) their O(MN/sup 3/) computational complexity (where M is the number of objectives and N is the population size); (2) their non-elitism approach; and (3) the need to specify a sharing parameter. In this paper, we suggest a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties. Specifically, a fast non-dominated sorting approach with O(MN/sup 2/) computational complexity is presented. Also, a selection operator is presented that creates a mating pool by combining the parent and offspring populations and selecting the best N solutions (with respect to fitness and spread). Simulation results on difficult test problems show that NSGA-II is able, for most problems, to find a much better spread of solutions and better convergence near the true Pareto-optimal front compared to the Pareto-archived evolution strategy and the strength-Pareto evolutionary algorithm - two other elitist MOEAs that pay special attention to creating a diverse Pareto-optimal front. Moreover, we modify the definition of dominance in order to solve constrained multi-objective problems efficiently. Simulation results of the constrained NSGA-II on a number of test problems, including a five-objective, seven-constraint nonlinear problem, are compared with another constrained multi-objective optimizer, and the much better performance of NSGA-II is observed. read more read less

Topics:

Sorting (57%)57% related to the paper, Evolutionary algorithm (56%)56% related to the paper, Mating pool (56%)56% related to the paper, Genetic algorithm (55%)55% related to the paper, Evolutionary computation (54%)54% related to the paper
View PDF
37,111 Citations
open accessOpen access Journal Article DOI: 10.1109/4235.585893
No free lunch theorems for optimization
David H. Wolpert1, William G. Macready1
IBM1

Abstract:

A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving. A number of "no free lunch" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class. These... A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving. A number of "no free lunch" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class. These theorems result in a geometric interpretation of what it means for an algorithm to be well suited to an optimization problem. Applications of the NFL theorems to information-theoretic aspects of optimization and benchmark measures of performance are also presented. Other issues addressed include time-varying optimization problems and a priori "head-to-head" minimax distinctions between optimization algorithms, distinctions that result despite the NFL theorems' enforcing of a type of uniformity over all algorithms. read more read less

Topics:

No free lunch in search and optimization (66%)66% related to the paper, No free lunch theorem (66%)66% related to the paper, Optimization problem (58%)58% related to the paper, Evolutionary computation (52%)52% related to the paper
View PDF
10,771 Citations
Journal Article DOI: 10.1109/4235.985692
The particle swarm - explosion, stability, and convergence in a multidimensional complex space
M. Clerc1, James Kennedy2

Abstract:

The particle swarm is an algorithm for finding optimal regions of complex search spaces through the interaction of individuals in a population of particles. This paper analyzes a particle's trajectory as it moves in discrete time (the algebraic view), then progresses to the view of it in continuous time (the analytical view).... The particle swarm is an algorithm for finding optimal regions of complex search spaces through the interaction of individuals in a population of particles. This paper analyzes a particle's trajectory as it moves in discrete time (the algebraic view), then progresses to the view of it in continuous time (the analytical view). A five-dimensional depiction is developed, which describes the system completely. These analyses lead to a generalized model of the algorithm, containing a set of coefficients to control the system's convergence tendencies. Some results of the particle swarm optimizer, implementing modifications derived from the analysis, suggest methods for altering the original algorithm in ways that eliminate problems and increase the ability of the particle swarm to find optima of some well-studied test functions. read more read less

Topics:

Multi-swarm optimization (65%)65% related to the paper, Particle swarm optimization (65%)65% related to the paper, Swarm behaviour (59%)59% related to the paper, Population (52%)52% related to the paper, Algorithm design (51%)51% related to the paper
8,287 Citations
Journal Article DOI: 10.1109/4235.585892
Ant colony system: a cooperative learning approach to the traveling salesman problem
Marco Dorigo1, Luca Maria Gambardella2

Abstract:

This paper introduces the ant colony system (ACS), a distributed algorithm that is applied to the traveling salesman problem (TSP). In the ACS, a set of cooperating agents called ants cooperate to find good solutions to TSPs. Ants cooperate using an indirect form of communication mediated by a pheromone they deposit on the ed... This paper introduces the ant colony system (ACS), a distributed algorithm that is applied to the traveling salesman problem (TSP). In the ACS, a set of cooperating agents called ants cooperate to find good solutions to TSPs. Ants cooperate using an indirect form of communication mediated by a pheromone they deposit on the edges of the TSP graph while building solutions. We study the ACS by running experiments to understand its operation. The results show that the ACS outperforms other nature-inspired algorithms such as simulated annealing and evolutionary computation, and we conclude comparing ACS-3-opt, a version of the ACS augmented with a local search procedure, to some of the best performing algorithms for symmetric and asymmetric TSPs. read more read less

Topics:

Ant colony optimization algorithms (60%)60% related to the paper, Ant colony (58%)58% related to the paper, Parallel metaheuristic (56%)56% related to the paper, Travelling salesman problem (56%)56% related to the paper, Extremal optimization (53%)53% related to the paper
View PDF
7,596 Citations
Journal Article DOI: 10.1109/4235.797969
Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach
Eckart Zitzler1, Lothar Thiele1

Abstract:

Evolutionary algorithms (EAs) are often well-suited for optimization problems involving several, often conflicting objectives. Since 1985, various evolutionary approaches to multiobjective optimization have been developed that are capable of searching for multiple solutions concurrently in a single run. However, the few compa... Evolutionary algorithms (EAs) are often well-suited for optimization problems involving several, often conflicting objectives. Since 1985, various evolutionary approaches to multiobjective optimization have been developed that are capable of searching for multiple solutions concurrently in a single run. However, the few comparative studies of different methods presented up to now remain mostly qualitative and are often restricted to a few approaches. In this paper, four multiobjective EAs are compared quantitatively where an extended 0/1 knapsack problem is taken as a basis. Furthermore, we introduce a new evolutionary approach to multicriteria optimization, the strength Pareto EA (SPEA), that combines several features of previous multiobjective EAs in a unique manner. It is characterized by (a) storing nondominated solutions externally in a second, continuously updated population, (b) evaluating an individual's fitness dependent on the number of external nondominated points that dominate it, (c) preserving population diversity using the Pareto dominance relationship, and (d) incorporating a clustering procedure in order to reduce the nondominated set without destroying its characteristics. The proof-of-principle results obtained on two artificial problems as well as a larger problem, the synthesis of a digital hardware-software multiprocessor system, suggest that SPEA can be very effective in sampling from along the entire Pareto-optimal front and distributing the generated solutions over the tradeoff surface. Moreover, SPEA clearly outperforms the other four multiobjective EAs on the 0/1 knapsack problem. read more read less

Topics:

Evolutionary algorithm (60%)60% related to the paper, Multi-objective optimization (57%)57% related to the paper, Evolutionary computation (56%)56% related to the paper, Knapsack problem (56%)56% related to the paper, Optimization problem (54%)54% related to the paper
7,512 Citations
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IEEE Transactions on Evolutionary Computation format uses IEEEtran citation style.

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Frequently asked questions

1. Can I write IEEE Transactions on Evolutionary Computation in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the IEEE Transactions on Evolutionary Computation guidelines and auto format it.

2. Do you follow the IEEE Transactions on Evolutionary Computation guidelines?

Yes, the template is compliant with the IEEE Transactions on Evolutionary Computation guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in IEEE Transactions on Evolutionary Computation?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the IEEE Transactions on Evolutionary Computation citation style.

4. Can I use the IEEE Transactions on Evolutionary Computation templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for IEEE Transactions on Evolutionary Computation.

5. Can I use a manuscript in IEEE Transactions on Evolutionary Computation that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper IEEE Transactions on Evolutionary Computation that you can download at the end.

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It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in IEEE Transactions on Evolutionary Computation.

7. Where can I find the template for the IEEE Transactions on Evolutionary Computation?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per IEEE Transactions on Evolutionary Computation's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the IEEE Transactions on Evolutionary Computation's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

9. IEEE Transactions on Evolutionary Computation an online tool or is there a desktop version?

SciSpace's IEEE Transactions on Evolutionary Computation is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

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After writing your paper autoformatting in IEEE Transactions on Evolutionary Computation, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is IEEE Transactions on Evolutionary Computation's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for IEEE Transactions on Evolutionary Computation?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for IEEE Transactions on Evolutionary Computation. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In IEEE Transactions on Evolutionary Computation?

The 5 most common citation types in order of usage for IEEE Transactions on Evolutionary Computation are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

15. How do I submit my article to the IEEE Transactions on Evolutionary Computation?

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16. Can I download IEEE Transactions on Evolutionary Computation in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in IEEE Transactions on Evolutionary Computation Endnote style according to Elsevier guidelines.

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