IEEE Transaction on Evolutionary Computation - Impact Factor

IEEE Transactions on Evolutionary Computation | 2024 Impact Factor:12 | Cite Score:23.5 | Q1

IEEE Transactions on Evolutionary Computation Journal

Impact Factor and Journal Rank of IEEE Transactions on Evolutionary Computation

About: The IEEE Transactions on Evolutionary Computation(TEVC) is a prestigious scholarly journal published by the IEEE Computational Intelligence Society. It is dedicated to the dissemination of high-quality research in the field of evolutionary computation and related areas.
Objective: The primary objective of TEVC is to publish original research articles, surveys, and reviews that advance the understanding and application of evolutionary computation techniques.
Content: TEVC publishes original research contributions that present novel algorithms, methodologies, theoretical insights, and applications of evolutionary computation techniques. The journal also features survey papers that provide comprehensive overviews of specific research areas and review papers that critically evaluate the state-of-the-art in evolutionary computation.
Interdisciplinary Focus: While primarily focused on evolutionary computation, TEVC adopts an interdisciplinary approach, welcoming contributions from related fields such as artificial intelligence, machine learning, optimization, robotics, and bioinformatics. This interdisciplinary perspective facilitates the integration of evolutionary computation techniques into diverse application domains.
High Standards and Rigorous Review: TEVC maintains high standards of quality and rigor through a rigorous peer-review process. Each submitted manuscript undergoes thorough evaluation by experts in the field, ensuring that only the highest-quality research is published.
Global Reach and Impact: As part of the IEEE Computational Intelligence Society, TEVC enjoys a broad international readership and impact. It attracts contributions from leading researchers and institutions worldwide, fostering collaboration and knowledge exchange on a global scale.
Educational Resource: TEVC serves as an essential resource for students, researchers, and practitioners interested in evolutionary computation. The journals comprehensive coverage of theory, methodologies, and applications provides valuable insights and guidance for both academic study and practical implementation.

Journal Home: Journal Homepage

Editor-in-Chief: Professor Carlos A. Coello

Scope: IEEE Transactions on Evolutionary Computation (TEVC) is a scholarly journal that focuses on the study and application of evolutionary computation (EC) techniques. EC is a subfield of artificial intelligence and computational intelligence that draws inspiration from the principles of natural evolution to solve complex optimization and search problems.
The scope of TEVC covers a wide range of topics related to evolutionary computation, including but not limited to:
Genetic Algorithms (GA): Theory, design, implementation, and analysis of genetic algorithms, which are population-based optimization algorithms inspired by the process of natural selection and genetics.
Evolution Strategies (ES): Research on evolution strategies, which are optimization algorithms that use strategies such as mutation, recombination, and selection to iteratively improve candidate solutions.
Genetic Programming (GP): Development and application of genetic programming techniques, which evolve computer programs or mathematical expressions to solve problems in various domains.
Differential Evolution (DE): Investigation of differential evolution algorithms, which are population-based optimization techniques that iteratively improve candidate solutions using differential operators.
Evolutionary Optimization: Research on other variants of evolutionary optimization algorithms, such as evolutionary algorithms, memetic algorithms, ant colony optimization, particle swarm optimization, and other nature-inspired optimization techniques.
Multi-objective Evolutionary Algorithms: Study of evolutionary algorithms designed to optimize multiple conflicting objectives simultaneously, considering trade-offs between different objectives.
Parallel and Distributed Evolutionary Algorithms: Design and analysis of evolutionary algorithms that leverage parallel and distributed computing architectures to accelerate optimization processes and handle large-scale optimization problems.