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
11.
Neumann A, Neumann F
Evol Comput
. 2024 Sep;
:1-35.
PMID: 39316732
Many real-world optimization problems can be stated in terms of submodular functions. Furthermore, these real-world problems often involve uncertainties which may lead to the violation of given constraints. A lot...
12.
Wang P, Xue B, Liang J, Zhang M
Evol Comput
. 2024 Sep;
:1-27.
PMID: 39298735
Performing classification on high-dimensional data poses a significant challenge due to the huge search space. Moreover, complex feature interactions introduce an additional obstacle. The problems can be addressed by using...
13.
Nguyen B, Xue B, Zhang M
Evol Comput
. 2024 Aug;
:1-33.
PMID: 39172076
In classification, feature selection is an essential pre-processing step that selects a small subset of features to improve classification performance. Existing feature selection approaches can be divided into three main...
14.
Pitra Z, Koza J, Tumpach J, Holena M
Evol Comput
. 2024 Aug;
:1-29.
PMID: 39141842
Surrogate modeling has become a valuable technique for black-box optimization tasks with expensive evaluation of the objective function. In this paper, we investigate the relationships between the predictive accuracy of...
15.
Kostovska A, Vermetten D, Korosec P, Dzeroski S, Doerr C, Eftimov T
Evol Comput
. 2024 Aug;
:1-27.
PMID: 39101902
Modular algorithm frameworks not only allow for combinations never tested in manually selected algorithm portfolios, but they also provide a structured approach to assess which algorithmic ideas are crucial for...
16.
Neumann F, Witt C
Evol Comput
. 2024 Aug;
:1-22.
PMID: 39101892
Chance constrained optimization problems allow to model problems where constraints involving stochastic components should only be violated with a small probability. Evolutionary algorithms have been applied to this scenario and...
17.
Gu H, Wang H, He C, Yuan B, Jin Y
Evol Comput
. 2024 Jun;
:1-25.
PMID: 38889350
Recently, computationally intensive multiobjective optimization problems have been efficiently solved by surrogate-assisted multiobjective evolutionary algorithms. However, most of those algorithms can only handle no more than 200 decision variables. As...
18.
Roy N, Beauthier C, Mayer A
Evol Comput
. 2024 Jun;
:1-30.
PMID: 38889349
Heuristic optimization methods such as Particle Swarm Optimization depend on their parameters to achieve optimal performance on a given class of problems. Some modifications of heuristic algorithms aim at adapting...
19.
Li C, Sun J, Li L, Shan M, Palade V, Wu X
Evol Comput
. 2024 May;
32(4):427-458.
PMID: 38776458
Premature convergence is a thorny problem for particle swarm optimization (PSO) algorithms, especially on multimodal problems, where maintaining swarm diversity is crucial. However, most enhancement strategies for PSO, including the...
20.
Marrero A, Segredo E, Leon C, Hart E
Evol Comput
. 2024 May;
:1-41.
PMID: 38713741
Gathering sufficient instance data to either train algorithm-selection models or understand algorithm footprints within an instance space can be challenging. We propose an approach to generating synthetic instances that are...