Example of Adaptive Behavior format
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Example of Adaptive Behavior format Example of Adaptive Behavior format Example of Adaptive Behavior format Example of Adaptive Behavior format Example of Adaptive Behavior format Example of Adaptive Behavior format Example of Adaptive Behavior format Example of Adaptive Behavior format Example of Adaptive Behavior format Example of Adaptive Behavior format Example of Adaptive Behavior format
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Example of Adaptive Behavior format Example of Adaptive Behavior format Example of Adaptive Behavior format Example of Adaptive Behavior format Example of Adaptive Behavior format Example of Adaptive Behavior format Example of Adaptive Behavior format Example of Adaptive Behavior format Example of Adaptive Behavior format Example of Adaptive Behavior format Example of Adaptive Behavior format
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open access Open Access ISSN: 10597123 e-ISSN: 17412633
recommended Recommended

Adaptive Behavior — Template for authors

Publisher: SAGE
Categories Rank Trend in last 3 yrs
Philosophy #35 of 644 down down by None rank
Experimental and Cognitive Psychology #87 of 148 down down by 4 ranks
Artificial Intelligence #142 of 227 down down by None rank
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 103 Published Papers | 247 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 02/07/2020
Insights & related journals
General info
Top papers
Popular templates
Get started guide
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FAQ

Journal Performance & Insights

  • Impact Factor
  • CiteRatio
  • SJR
  • SNIP

Impact factor 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.

0.929

32% from 2018

Impact factor for Adaptive Behavior from 2016 - 2019
Year Value
2019 0.929
2018 1.362
2017 1.0
2016 1.019
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has decreased by 32% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

CiteRatio is a measure of average citations received per peer-reviewed paper published in the journal.

2.4

14% from 2019

CiteRatio for Adaptive Behavior from 2016 - 2020
Year Value
2020 2.4
2019 2.8
2018 2.6
2017 2.3
2016 2.7
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has decreased by 14% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR) measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

0.286

9% from 2019

SJR for Adaptive Behavior from 2016 - 2020
Year Value
2020 0.286
2019 0.315
2018 0.345
2017 0.288
2016 0.454
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 9% in last years.
  • This journal’s SJR is in the top 10 percentile category.

Source Normalized Impact per Paper (SNIP) measures actual citations received relative to citations expected for the journal's category.

0.689

0% from 2019

SNIP for Adaptive Behavior from 2016 - 2020
Year Value
2020 0.689
2019 0.691
2018 0.837
2017 0.607
2016 0.763
graph view Graph view
table view Table view

insights Insights

  • SNIP of this journal has decreased by 0% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Related Journals

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CiteRatio: 4.4 | SJR: 0.806 | SNIP: 2.211
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CiteRatio: 5.7 | SJR: 2.223 | SNIP: 2.098
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CiteRatio: 6.0 | SJR: 0.52 | SNIP: 1.569
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CiteRatio: 7.7 | SJR: 2.442 | SNIP: 2.279

Adaptive Behavior

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SAGE

Adaptive Behavior

Adaptive Behavior is a highly ranked, international peer reviewed journal that publishes original research and review articles on adaptive behavior in living organisms and autonomous artificial systems. For over 17 years it has offered ethologists, psychologists, behavioral ec...... Read More

Psychology

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Last updated on
01 Jul 2020
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ISSN
1059-7123
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Impact Factor
High - 1.164
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Open Access
No
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Sherpa RoMEO Archiving Policy
Green faq
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Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Bibliography Name
SageV
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Citation Type
Author Year
(Blonder et al., 1982)
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Bibliography Example
Blonder, G. E., Tinkham, M., and Klapwijk, T. M. (1982). Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and super- current conversion. Phys. Rev. B, 25(7):4515–4532.

Top papers written in this journal

Journal Article DOI: 10.1177/105971239700500203
Ant-based load balancing in telecommunications networks
Ruud Schoonderwoerd1, Janet Bruten2, Owen Holland3, Léon J. M. Rothkrantz1
01 Sep 1996 - Adaptive Behavior

Abstract:

This article describes a novel method of achieving load balancing in telecommunications networks. A simulated network models a typical distribution of calls between nodes; nodes carrying an excess ... This article describes a novel method of achieving load balancing in telecommunications networks. A simulated network models a typical distribution of calls between nodes; nodes carrying an excess ... read more read less

Topics:

Load balancing (computing) (66%)66% related to the paper, Loss network (58%)58% related to the paper, Telecommunications network (57%)57% related to the paper, Network model (50%)50% related to the paper
830 Citations
Journal Article DOI: 10.1177/105971239200100105
Evolving dynamical neural networks for adaptive behavior
Randall D. Beer1, John C. Gallagher1
30 Jun 1992 - Adaptive Behavior

Abstract:

We would like the behavior of the artificial agents that we construct to be as well-adapted to their environments as natural animals are to theirs. Unfortunately, designing controllers with these properties is a very difficult task. In this article, we demonstrate that continuous-time recurrent neural networks are a viable me... We would like the behavior of the artificial agents that we construct to be as well-adapted to their environments as natural animals are to theirs. Unfortunately, designing controllers with these properties is a very difficult task. In this article, we demonstrate that continuous-time recurrent neural networks are a viable mechanism for adaptive agent control and that the genetic algorithm can be used to evolve effective neural controllers. A significant advantage of this approach is that one need specify only a measure of an agent's overall performance rather than the precise motor output trajectories by which it is achieved. By manipulating the performance evaluation, one can place selective pressure on the development of controllers with desired properties. Several novel controllers have been evolved, including a chemotaxis controller that switches between different strategies depending on environmental conditions, and a locomotion controller that takes advantage of sensory feedback if available but th... read more read less

Topics:

Control theory (56%)56% related to the paper, Time delay neural network (55%)55% related to the paper, Recurrent neural network (54%)54% related to the paper, Artificial neural network (52%)52% related to the paper, Adaptive behavior (51%)51% related to the paper
552 Citations
Journal Article DOI: 10.1177/105971239700500305
Incremental Evolution of Complex General Behavior
Faustino Gomez1, Risto Mikkulainen1
01 Jan 1997 - Adaptive Behavior

Abstract:

Several researchers have demonstrated how complex action sequences can be learned through neuroevolution (i.e., evolving neural networks with genetic algorithms). However, complex general behavior such as evading predators or avoiding obstacles, which is not tied to specific environments, turns out to be very difficult to evo... Several researchers have demonstrated how complex action sequences can be learned through neuroevolution (i.e., evolving neural networks with genetic algorithms). However, complex general behavior such as evading predators or avoiding obstacles, which is not tied to specific environments, turns out to be very difficult to evolve. Often the system discovers mechanical strategies, such as moving back and forth, that help the agent cope but are not very effective, do not appear believable, and do not generalize to new environments. The problem is that a general strategy is too difficult for the evolution system to discover directly. This article proposes an approach wherein such complex general behavior is learned incrementally, by starting with simpler behavior and gradually making the task more challenging and general. The task transitions are implemented through successive stages of Delta coding (i.e., evolving modifications), which allows even converged populations to adapt to the new task. The method is... read more read less

Topics:

Neuroevolution (57%)57% related to the paper, Task (project management) (50%)50% related to the paper
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463 Citations
Journal Article DOI: 10.1177/1059712303114001
The Dynamics of Active Categorical Perception in an Evolved Model Agent
Randall D. Beer1
01 Dec 2003 - Adaptive Behavior

Abstract:

Notions of embodiment, situatedness, and dynamics are increasingly being debated in cognitive sci ence. However, these debates are often carried out in the absence of concrete examples. In order to... Notions of embodiment, situatedness, and dynamics are increasingly being debated in cognitive sci ence. However, these debates are often carried out in the absence of concrete examples. In order to... read more read less
439 Citations
open accessOpen access Journal Article DOI: 10.1177/105971230501300301
Reinforcement learning for RoboCup soccer keepaway
Peter Stone1, Richard S. Sutton2, Gregory Kuhlmann1
01 Sep 2005 - Adaptive Behavior

Abstract:

RoboCup simulated soccer presents many challenges to reinforcement learning methods, including a large state space, hidden and uncertain state, multiple independent agents learning simultaneously, and long and variable delays in the effects of actions. We describe our application of episodic SMDP Sarsa(λ) with linear tile-cod... RoboCup simulated soccer presents many challenges to reinforcement learning methods, including a large state space, hidden and uncertain state, multiple independent agents learning simultaneously, and long and variable delays in the effects of actions. We describe our application of episodic SMDP Sarsa(λ) with linear tile-coding function approximation and variable λ to learning higher-level decisions in a keepaway subtask of RoboCup soccer. In keepaway, one team, “the keepers,” tries to keep control of the ball for as long as possible despite the efforts of “the takers.” The keepers learn individually when to hold the ball and when to pass to a teammate. Our agents learned policies that significantly outperform a range of benchmark policies. We demonstrate the generality of our approach by applying it to a number of task variations including different field sizes and different numbers of players on each team. read more read less

Topics:

Reinforcement learning (54%)54% related to the paper
View PDF
419 Citations
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SciSpace is a very innovative solution to the formatting problem and existing providers, such as Mendeley or Word did not really evolve in recent years.

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What to expect from SciSpace?

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With SciSpace, you do not need a word template for Adaptive Behavior.

It automatically formats your research paper to SAGE formatting guidelines and citation style.

You can download a submission ready research paper in pdf, LaTeX and docx formats.

Time comparison

Time taken to format a paper and Compliance with guidelines

Plagiarism Reports via Turnitin

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Using this service, researchers can compare submissions against more than 170 million scholarly articles, a database of 70+ billion current and archived web pages. How Turnitin Integration works?

Turnitin Stats
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Adaptive Behavior format uses SageV citation style.

Automatically format and order your citations and bibliography in a click.

SciSpace allows imports from all reference managers like Mendeley, Zotero, Endnote, Google Scholar etc.

Frequently asked questions

Absolutely not! With our tool, you can freely write without having to focus on LaTeX. You can write your entire paper as per the Adaptive Behavior guidelines and autoformat it.

Yes. The template is fully compliant as per the guidelines of this journal. Our experts at SciSpace ensure that. Also, if there's any update in the journal format guidelines, we take care of it and include that in our algorithm.

Sure. We support all the top citation styles like APA style, MLA style, Vancouver style, Harvard style, Chicago style, etc. For example, in case of this journal, when you write your paper and hit autoformat, it will automatically update your article as per the Adaptive Behavior citation style.

You can avail our Free Trial for 7 days. I'm sure you'll find our features very helpful. Plus, it's quite inexpensive.

Yup. You can choose the right template, copy-paste the contents from the word doc and click on auto-format. You'll have a publish-ready paper that you can download at the end.

A matter of seconds. Besides that, our intuitive editor saves a load of your time in writing and formating your manuscript.

One little Google search can get you the Word template for any journal. However, why do you need a Word template when you can write your entire manuscript on SciSpace, autoformat it as per Adaptive Behavior's guidelines and download the same in Word, PDF and LaTeX formats? Try us out!.

Absolutely! You can do it using our intuitive editor. It's very easy. If you need help, you can always contact our support team.

SciSpace is an online tool for now. We'll soon release a desktop version. You can also request (or upvote) any feature that you think might be helpful for you and the research community in the feature request section once you sign-up with us.

Sure. You can request any template and we'll have it up and running within a matter of 3 working days. You can find the request box in the Journal Gallery on the right sidebar under the heading, "Couldn't find the format you were looking for?".

After you have written and autoformatted your paper, you can download it in multiple formats, viz., PDF, Docx and LaTeX.

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 those factors the review board, rejection rates, frequency of inclusion in indexes, Eigenfactor, etc. You must assess all the factors and then take the final call.

SHERPA/RoMEO Database

We have extracted this data from Sherpa Romeo to help our researchers understand the access level of this journal. The following table indicates the level of access a journal has as per Sherpa Romeo 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.

The 5 most common citation types in order of usage are:.

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

Our journal submission experts are skilled in submitting papers to various international journals.

After uploading your paper on SciSpace, you would see a button to request a journal submission service for Adaptive Behavior.

Each submission service is completed within 4 - 5 working days.

Yes. SciSpace provides this functionality.

After signing up, you would need to import your existing references from Word or .bib file.

SciSpace would allow download of your references in Adaptive Behavior Endnote style, according to sage guidelines.

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Typset automatically formats your research paper to Adaptive Behavior formatting guidelines and citation style.

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I spent hours with MS word for reformatting. It was frustrating - plain and simple. With SciSpace, I can draft my manuscripts and once it is finished I can just submit. In case, I have to submit to another journal it is really just a button click instead of an afternoon of reformatting.

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