Evolutionary Computation
Overview
Evolutionary Computation is a peer-reviewed journal that focuses on the study and application of computational methods inspired by biological evolution. It covers a wide range of topics including genetic algorithms, genetic programming, evolutionary strategies, and swarm intelligence. The journal publishes high-quality research articles, reviews, and case studies, providing a platform for researchers and practitioners to explore the advancements in this rapidly evolving field.
Details
Details
Abbr.
Evol Comput
Publisher
MIT Press
Start
1993
End
Continuing
Frequency
Quarterly
p-ISSN
1063-6560
e-ISSN
1530-9304
Country
United States
Language
English
Specialties
Biology
Public Health
Public Health
Metrics
Metrics
h-index / Ranks: 3682
90
SJR / Ranks: 2938
1170
CiteScore / Ranks: 3428
6.40
JIF / Ranks: 896
6.8
Recent Articles
1.
Cenikj G, Petelin G, Doerr C, Korosec P, Eftimov T
Evol Comput
. 2025 Mar;
:1-28.
PMID: 40053909
The representation of optimization problems and algorithms in terms of numerical features is a well-established tool for comparing optimization problem instances, for analyzing the behavior of optimization algorithms, and the...
2.
Santucci V, Ceberio J
Evol Comput
. 2025 Feb;
:1-30.
PMID: 39977581
Permutation problems have captured the attention of the combinatorial optimization community for decades due to the challenge they pose. Although their solutions are naturally encoded as permutations, in each problem,...
3.
Benavides X, Hernando L, Ceberio J, Lozano J
Evol Comput
. 2025 Feb;
:1-28.
PMID: 39977577
The Fourier transform over finite groups has proved to be a useful tool for analyzing combinatorial optimization problems. However, few heuristic and meta-heuristic algorithms have been proposed in the literature...
4.
Seiler M, Kerschke P, Trautmann H
Evol Comput
. 2025 Feb;
:1-27.
PMID: 39903851
In many recent works,the potential of Exploratory Landscape Analysis (ELA) features to numerically characterize single-objective continuous optimization problems has been demonstrated. These numerical features provide the input for all kinds...
5.
Huber J, Helenon F, Coninx M, Amar F, Doncieux S
Evol Comput
. 2025 Jan;
1-30.
PMID: 39823378
Quality-Diversity (QD) methods are algorithms that aim to generate a set of diverse and highperforming solutions to a given problem. Originally developed for evolutionary robotics, most QD studies are conducted...
6.
Kneissl C, Sudholt D
Evol Comput
. 2025 Jan;
1-29.
PMID: 39823377
Evolutionary algorithms make countless random decisions during selection, mutation and crossover operations. These random decisions require a steady stream of random numbers. We analyze the expected number of random bits...
7.
Larraga G, Miettinen K
Evol Comput
. 2025 Jan;
1-39.
PMID: 39823376
Interactive methods support decision-makers in finding the most preferred solution for multiobjective optimization problems, where multiple conflicting objective functions must be optimized simultaneously. These methods let a decision-maker provide preference...
8.
Huang Z, Zhou Y, Chen Z, Dang Q
Evol Comput
. 2025 Jan;
1-32.
PMID: 39823375
Decomposition-based multi-objective evolutionary algorithms (MOEAs) are popular methods utilized to address many-objective optimization problems (MaOPs). These algorithms decompose the original MaOP into several scalar optimization subproblems, and solve them to...
9.
Al-Helali B, Chen Q, Xue B, Zhang M
Evol Comput
. 2024 Nov;
:1-27.
PMID: 39571047
High-dimensionality is one of the serious real-world data challenges in symbolic regression and it is more challenging if the data are incomplete. Genetic programming has been successfully utilised for high-dimensional...
10.
Hamano R, Uchida K, Shirakawa S, Morinaga D, Akimoto Y
Evol Comput
. 2024 Oct;
:1-52.
PMID: 39353171
The majority of theoretical analyses of evolutionary algorithms in the discrete domain focus on binary optimization algorithms, even though black-box optimization on the categorical domain has a lot of practical...