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open access Open Access ISSN: 13892576 e-ISSN: 15737632

Genetic Programming and Evolvable Machines — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Theoretical Computer Science #42 of 120 down down by 16 ranks
Computer Science Applications #260 of 693 down down by 96 ranks
Hardware and Architecture #76 of 157 down down by 31 ranks
Software #190 of 389 down down by 62 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 82 Published Papers | 288 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 16/07/2020
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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.

1.781

34% from 2018

Impact factor for Genetic Programming and Evolvable Machines from 2016 - 2019
Year Value
2019 1.781
2018 1.333
2017 1.455
2016 1.514
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 34% 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.

3.5

3% from 2019

CiteRatio for Genetic Programming and Evolvable Machines from 2016 - 2020
Year Value
2020 3.5
2019 3.6
2018 3.1
2017 3.7
2016 3.7
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has decreased by 3% 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.

0.371

12% from 2019

SJR for Genetic Programming and Evolvable Machines from 2016 - 2020
Year Value
2020 0.371
2019 0.33
2018 0.302
2017 0.376
2016 0.426
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has increased by 12% 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.

1.21

14% from 2019

SNIP for Genetic Programming and Evolvable Machines from 2016 - 2020
Year Value
2020 1.21
2019 1.058
2018 0.872
2017 1.159
2016 1.514
graph view Graph view
table view Table view

insights Insights

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

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Genetic Programming and Evolvable Machines

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Springer

Genetic Programming and Evolvable Machines

The journal of Genetic Programming and Evolvable Machines is devoted to reporting innovative and significant progress in automatic evolution of software and hardware. Methods for artificial evolution of active components, such as programs or machines, are rapidly developing br...... Read More

Theoretical Computer Science

Hardware and Architecture

Computer Science Applications

Software

Mathematics

i
Last updated on
16 Jul 2020
i
ISSN
1389-2576
i
Impact Factor
Very High - 3.587
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
SPBASIC
i
Citation Type
Author Year
(Blonder et al, 1982)
i
Bibliography Example
Beenakker CWJ (2006) Specular andreev reflection in graphene. Phys Rev Lett 97(6):067,007, URL 10.1103/PhysRevLett.97.067007

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1007/S10710-007-9028-8
Compositional pattern producing networks: A novel abstraction of development
Kenneth O. Stanley1

Abstract:

Natural DNA can encode complexity on an enormous scale. Researchers are attempting to achieve the same representational efficiency in computers by implementing developmental encodings, i.e. encodings that map the genotype to the phenotype through a process of growth from a small starting point to a mature form. A major challe... Natural DNA can encode complexity on an enormous scale. Researchers are attempting to achieve the same representational efficiency in computers by implementing developmental encodings, i.e. encodings that map the genotype to the phenotype through a process of growth from a small starting point to a mature form. A major challenge in in this effort is to find the right level of abstraction of biological development to capture its essential properties without introducing unnecessary inefficiencies. In this paper, a novel abstraction of natural development, called Compositional Pattern Producing Networks (CPPNs), is proposed. Unlike currently accepted abstractions such as iterative rewrite systems and cellular growth simulations, CPPNs map to the phenotype without local interaction, that is, each individual component of the phenotype is determined independently of every other component. Results produced with CPPNs through interactive evolution of two-dimensional images show that such an encoding can nevertheless produce structural motifs often attributed to more conventional developmental abstractions, suggesting that local interaction may not be essential to the desirable properties of natural encoding in the way that is usually assumed. read more read less

Topics:

HyperNEAT (51%)51% related to the paper, Artificial development (51%)51% related to the paper
View PDF
616 Citations
Journal Article DOI: 10.1007/S10710-005-6164-X
Solving Multiobjective Optimization Problems Using an Artificial Immune System

Abstract:

In this paper, we propose an algorithm based on the clonal selection principle to solve multiobjective optimization problems (either constrained or unconstrained). The proposed approach uses Pareto dominance and feasibility to identify solutions that deserve to be cloned, and uses two types of mutation: uniform mutation is ap... In this paper, we propose an algorithm based on the clonal selection principle to solve multiobjective optimization problems (either constrained or unconstrained). The proposed approach uses Pareto dominance and feasibility to identify solutions that deserve to be cloned, and uses two types of mutation: uniform mutation is applied to the clones produced and non-uniform mutation is applied to the ?not so good? antibodies (which are represented by binary strings that encode the decision variables of the problem to be solved). We also use a secondary (or external) population that stores the nondominated solutions found along the search process. Such secondary population constitutes the elitist mechanism of our approach and it allows it to move towards the true Pareto front of a problem over time. Our approach is compared with three other algorithms that are representative of the state-of-the-art in evolutionary multiobjective optimization. For our comparative study, three metrics are adopted and graphical comparisons with respect to the true Pareto front of each problem are also included. Results indicate that the proposed approach is a viable alternative to solve multiobjective optimization problems. read more read less

Topics:

Multi-objective optimization (60%)60% related to the paper, Pareto principle (57%)57% related to the paper, Population (53%)53% related to the paper
View PDF
562 Citations
Journal Article DOI: 10.1023/A:1010066330916
Principles in the Evolutionary Design of Digital Circuits—Part II
Julian F. Miller1, Dominic Job2, Vesselin K. Vassilev2

Abstract:

In a previous work it was argued that by studying evolved designs of gradually increasing scale, one might be able to discern new, efficient, and generalisable principles of design. These ideas are tested in the context of designing digital circuits, particularly arithmetic circuits. This process of discovery is seen as a pri... In a previous work it was argued that by studying evolved designs of gradually increasing scale, one might be able to discern new, efficient, and generalisable principles of design. These ideas are tested in the context of designing digital circuits, particularly arithmetic circuits. This process of discovery is seen as a principle extraction loop in which the evolved data is analysed both phenotypically and genotypically by processes of data mining and landscape analysis. The information extracted is then fed back into the evolutionary algorithm to enhance its search capabilities and hence increase the likelihood of identifying new principles which explain how to build systems which are too large to evolve. read more read less

Topics:

Evolutionary computation (51%)51% related to the paper, Evolutionary algorithm (51%)51% related to the paper, Fitness landscape (51%)51% related to the paper
401 Citations
open accessOpen access Journal Article DOI: 10.1023/B:GENP.0000030197.83685.94
Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm
Andrew Watkins1, Jon Timmis1, Lois Boggess2

Abstract:

This paper presents the inception and subsequent revisions of an immune-inspired supervised learning algorithm, Artificial Immune Recognition System (AIRS). It presents the immunological components that inspired the algorithm and describes the initial algorithm in detail. The discussion then moves to revisions of the basic al... This paper presents the inception and subsequent revisions of an immune-inspired supervised learning algorithm, Artificial Immune Recognition System (AIRS). It presents the immunological components that inspired the algorithm and describes the initial algorithm in detail. The discussion then moves to revisions of the basic algorithm that remove certain unnecessary complications of the original version. Experimental results for both versions of the algorithm are discussed and these results indicate that the revisions to the algorithm do not sacrifice accuracy while increasing the data reduction capabilities of AIRS. read more read less

Topics:

Artificial immune system (69%)69% related to the paper, Supervised learning (62%)62% related to the paper, Artificial neural network (57%)57% related to the paper
View PDF
372 Citations
Journal Article DOI: 10.1007/S10710-007-9035-9
Evolutionary computation: a unified approach
Colin R. Reeves1

Abstract:

While Lawrence Fogel, John Holland, Ingo Rechenberg and others were the undoubted pioneers of the field we now know as evolutionary algorithms (EA), or evolutionary computation (EC), Ken De Jong’s doctoral thesis of 1975 deserves much of the credit for firing the enthusiasm of several research communities in the practical exp... While Lawrence Fogel, John Holland, Ingo Rechenberg and others were the undoubted pioneers of the field we now know as evolutionary algorithms (EA), or evolutionary computation (EC), Ken De Jong’s doctoral thesis of 1975 deserves much of the credit for firing the enthusiasm of several research communities in the practical exploration of these methods. Moreover, as he has taken a very active part in the development of the field through the last 30 years, there could scarcely be anyone better placed to write a book on evolutionary computation. As the subtitle of his book promises, De Jong takes a unified approach. His first 4 chapters carefully explain and differentiate, whilst putting in their historical context, the common aspects of different EC paradigms (evolutionary programming—EP, evolution strategies—ES and genetic algorithms—GA). Chapters 1–4 use clear examples, rather than too many mathematical symbols. They form a truly superb introduction. Any novice coming to EC should come away with an excellent grasp of the basics. In chapter 5 he discusses the different uses to which EAs have been put as problem-solvers. The greater part is devoted to optimization (OPT-EA), with shorter sections on search, machine learning, and automated programming. There is a final, very brief, section on adaptive EAs. In the optimization part, considerable care is taken in the organisation of his material—again, presumably, with the novice in mind. Chapter 6 is the longest, and focuses on EC theory. De Jong carefully builds up a picture of the influences of selection, mutation and recombination on the behaviour of EAs. If you are expecting theory in the sense of a comprehensive, general model with well-understood effects, you will be disappointed. There are equations, but the argument is in fact founded on a series of experiments, whose results are displayed in a series of graphs. That is not to say that the insights gained are incorrect, or read more read less

Topics:

Human-based evolutionary computation (82%)82% related to the paper, Interactive evolutionary computation (80%)80% related to the paper, Evolutionary programming (76%)76% related to the paper, Java Evolutionary Computation Toolkit (73%)73% related to the paper, Evolutionary acquisition of neural topologies (73%)73% related to the paper
365 Citations
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