TEVC logo

 

Scope

IEEE Transactions on Evolutionary Computation (TEVC) publishes archival quality original papers in evolutionary computation and related areas including nature-inspired algorithms, population-based methods, and optimization where selection and variation are integral, and hybrid systems where these paradigms are combined. Purely theoretical papers are considered as are application papers that provide general insights into these areas of computation.

The IEEE TEVC, published six times a year, publishes archival quality original papers in evolutionary computation and related areas including nature-inspired algorithms, population-based methods, and optimization where selection and variation are integral, and hybrid systems where these paradigms are combined. Purely theoretical papers are considered as are application papers that provide general insights into these areas of computation.

Impact Score

TEVC impact score 2019

Journal Citation Metrics Journal Citation Metrics such as Impact Factor, Eigenfactor Score™ and Article Influence Score™ are available where applicable. Each year, Journal Citation Reports© (JCR) from Thomson Reuters examines the influence and impact of scholarly research journals. JCR reveals the relationship between citing and cited journals, offering a systematic, objective means to evaluate the world's leading journals. Find out more about IEEE Journal Rankings.

 

Featured Paper

Data-Driven Evolutionary Optimization: An Overview and Case Studies
Authors: Yaochu Jin, Handing Wang, Tinkle Chugh, Dan Guo, Kaisa Miettinen
Publication: IEEE Transactions on Evolutionary Computation (TEVC)
Issue: Volume 23, Issue 3 – June 2019
Pages: 442-458

Abstract: Most evolutionary optimization algorithms assume that the evaluation of the objective and constraint functions is straightforward. In solving many real-world optimization problems, however, such objective functions may not exist. Instead, computationally expensive numerical simulations or costly physical experiments must be performed for fitness evaluations. In more extreme cases, only historical data are available for performing optimization and no new data can be generated during optimization. Solving evolutionary optimization problems driven by data collected in simulations, physical experiments, production processes, or daily life are termed data-driven evolutionary optimization. In this paper, we provide a taxonomy of different data driven evolutionary optimization problems, discuss main challenges in data-driven evolutionary optimization with respect to the nature and amount of data, and the availability of new data during optimization. Real-world application examples are given to illustrate different model management strategies for different categories of data-driven optimization problems.

Index Terms: Data science, data-driven optimization, evolutionary algorithms (EAs), machine learning, model management, surrogate
IEEE Xplore Link: https://ieeexplore.ieee.org/document/8456559