Evolutionary Computation

Aims & Scope

Evolutionary Computation is a leading journal in its field.

It provides an international forum for facilitating and enhancing the exchange of information among researchers involved in both the theoretical and practical aspects of computational systems drawing their inspiration from nature, with particular emphasis on evolutionary models of computation such as genetic algorithms, evolutionary strategies, classifier systems, evolutionary programming, and genetic programming.

It welcomes articles from related fields such as swarm intelligence (e.g.

Ant Colony Optimization and Particle Swarm Optimization), and other nature-inspired computation paradigms (e.g.

Artificial Immune Systems).

As well as publishing articles describing theoretical and/or experimental work, the journal also welcomes application-focused papers describing breakthrough results in an application domain or methodological papers where the specificities of the real-world problem led to significant algorithmic improvements that could possibly be generalized to other areas.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	3.4
2024	4.60

SJR (SCImago Journal Rank)

Year	Value
2024	0.713

Quartile

Year	Value
2024	Q2

h-index

Year	Value
2024	93

Journal Rank

Year	Value
2024	7923

Journal Citation Indicator

Year	Value
2024	207

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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
• Medline

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in 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.

• Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms

  Citation: 5455

  Authors: N., Kalyanmoy

• Comparison of Multiobjective Evolutionary Algorithms: Empirical Results

  Citation: 4473

  Authors: Eckart, Kalyanmoy, Lothar

• Completely Derandomized Self-Adaptation in Evolution Strategies

  Citation: 3280

  Authors: Nikolaus, Andreas

• Evolving Neural Networks through Augmenting Topologies

  Citation: 2252

  Authors: Kenneth O., Risto

• Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES)

  Citation: 1969

  Authors: Nikolaus, Sibylle D., Petros

• Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy

  Citation: 1779

  Authors: Joshua D., David W.

• HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization

  Citation: 1761

  Authors: Johannes, Eckart

• An Overview of Evolutionary Algorithms in Multiobjective Optimization

  Citation: 1669

  Authors: Carlos M., Peter J.

• An Overview of Evolutionary Algorithms for Parameter Optimization

  Citation: 1566

  Authors: Thomas, Hans-Paul

• Evolutionary Algorithms for Constrained Parameter Optimization Problems

  Citation: 1337

  Authors: Zbigniew, Marc

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