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IEEE Transactions on Evolutionary Computation is a peer-reviewed journal published by IEEE, covering Evolutionary Algorithms and Applications, Metaheuristic Optimization Algorithms Research, Advanced Multi-Objective Optimization Algorithms. Over its lifetime it has published 3,256 papers, which have received 376,963 citations.
| Publisher | IEEE |
|---|---|
| Reference style | Numbered (IEEE) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, and C. Lee, "A representative article title," IEEE Transactions on Evolutionary Computation, vol. 12, no. 3, pp. 45–58, 2023.
Formats any DOI in IEEE Transactions on Evolutionary Computation style. No sign-up. |
| Abbreviation (ISO-4) | TEVC |
| Publishes research in | Evolutionary Algorithms and Applications Metaheuristic Optimization Algorithms Research Advanced Multi-Objective Optimization Algorithms Diverse Scientific and Economic Studies Ferroelectric and Negative Capacitance Devices |
| ISSN | 1089-778X |
| Citation impact (2-yr) | 5.44 |
| h-index | 248 |
| i10-index | 1,694 |
| Top institutions publishing here | Southern University of Science and Technology |
| Journal website | cis.ieee.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Multi-objective evolutionary algorithms (MOEAs) that use non-dominated sorting and sharing have been criticized mainly for: (1) their O(MN/sup 3/) computational complexity (where M is the number of objectives and N is the population size); (2) their non-elitism approach; and (3) the need to specify a sharing parameter. In this paper, we suggest a non-dominated sorting-based…
A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving. A number of "no free lunch" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class. These theorems result in a geometric…
Decomposition is a basic strategy in traditional multiobjective optimization. However, it has not yet been widely used in multiobjective evolutionary optimization. This paper proposes a multiobjective evolutionary algorithm based on decomposition (MOEA/D). It decomposes a multiobjective optimization problem into a number of scalar optimization subproblems and optimizes them simultaneously. Each subproblem is optimized by only…
The particle swarm is an algorithm for finding optimal regions of complex search spaces through the interaction of individuals in a population of particles. This paper analyzes a particle's trajectory as it moves in discrete time (the algebraic view), then progresses to the view of it in continuous time (the analytical view). A five-dimensional depiction…
Evolutionary algorithms (EAs) are often well-suited for optimization problems involving several, often conflicting objectives. Since 1985, various evolutionary approaches to multiobjective optimization have been developed that are capable of searching for multiple solutions concurrently in a single run. However, the few comparative studies of different methods presented up to now remain mostly qualitative and are…
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