Genetic Programming and Evolvable Machines

Aims & Scope

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 branches of adaptive computation and adaptive engineering.

They entail the development, evaluation and application of methods that mirror the process of neo-Darwinian evolution and produce as a result computational expressions such as algorithms or machines such as mechanical or electronic devices that actively process environmental information and transform their environment.

In addition to its main topics, the journal covers related topics such as evolutionary algorithms with variable-size genomes, alternate methods of program induction, approaches to engineering systems development based on embryology, morphogenesis or other techniques inspired by adaptive natural systems.

View Aims & Scope

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Metrics & Ranking

SJR (SCImago Journal Rank)

Year	Value
2024	0.394

Quartile

Year	Value
2024	Q3

h-index

Year	Value
2024	45

Impact Factor

Year	Value
2024	1.70

Journal Rank

Year	Value
2024	13941

Journal Citation Indicator

Year	Value
2024	124

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science and Mathematics, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Solving Multiobjective Optimization Problems Using an Artificial Immune System

  Citation: 839

  Authors: Carlos A. Coello, Nareli Cruz

• Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep learning

  Citation: 673

  Authors: Jeff

• Compositional pattern producing networks: A novel abstraction of development

  Citation: 516

  Authors: Kenneth O.

• Grammar-based Genetic Programming: a survey

  Citation: 284

  Authors: Robert I., Nguyen Xuan, Peter Alexander, Yin, Michael

• Using DNA to Power Nanostructures

  Citation: 280

  Authors: Bernard, Allen P.

• Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm

  Citation: 264

  Authors: Andrew, Jon, Lois

• Human-competitive results produced by genetic programming

  Citation: 242

  Authors: John R.

• Semantically-based crossover in genetic programming: application to real-valued symbolic regression

  Citation: 229

  Authors: Nguyen Quang, Nguyen Xuan, Michael, R. I., Edgar

• Anomaly Detection Using Real-Valued Negative Selection

  Citation: 223

  Authors: Fabio A., Dipankar

• Genetic Programming and Autoconstructive Evolution with the Push Programming Language

  Citation: 203

  Authors: Lee, Alan

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