Evolutionary multitasking via explicit autoencoding
Evolutionary multitasking (EMT) is an emerging research topic in the field of evolutionary
computation. In contrast to the traditional single-task evolutionary search, EMT conducts …
computation. In contrast to the traditional single-task evolutionary search, EMT conducts …
Interval multiobjective optimization with memetic algorithms
J Sun, Z Miao, D Gong, XJ Zeng, J Li… - IEEE transactions on …, 2019 - ieeexplore.ieee.org
One of the most important and widely faced optimization problems in real applications is the
interval multiobjective optimization problems (IMOPs). The state-of-the-art evolutionary …
interval multiobjective optimization problems (IMOPs). The state-of-the-art evolutionary …
Review of the research landscape of multi-criteria evaluation and benchmarking processes for many-objective optimization methods: coherent taxonomy, challenges …
RT Mohammed, R Yaakob, AA Zaidan… - … Journal of Information …, 2020 - World Scientific
Evaluation and benchmarking of many-objective optimization (MaOO) methods are
complicated. The rapid development of new optimization algorithms for solving problems …
complicated. The rapid development of new optimization algorithms for solving problems …
A multifactorial optimization framework based on adaptive intertask coordinate system
The searching ability of the population-based search algorithms strongly relies on the
coordinate system on which they are implemented. However, the widely used coordinate …
coordinate system on which they are implemented. However, the widely used coordinate …
Termination detection strategies in evolutionary algorithms: a survey
Y Liu, A Zhou, H Zhang - Proceedings of the Genetic and Evolutionary …, 2018 - dl.acm.org
This paper provides an overview of developments on termination conditions in evolutionary
algorithms (EAs). It seeks to give a representative picture of the termination conditions in …
algorithms (EAs). It seeks to give a representative picture of the termination conditions in …
Entropy based evolutionary algorithm with adaptive reference points for many-objective optimization problems
C Zhou, G Dai, C Zhang, X Li, K Ma - Information Sciences, 2018 - Elsevier
Many-objective optimization problems (MaOPs) have attracted more and more attention due
to its challenges for multi-objective evolutionary algorithms. Reference points or weight …
to its challenges for multi-objective evolutionary algorithms. Reference points or weight …
A self-adaptive discrete PSO algorithm with heterogeneous parameter values for dynamic TSP
This paper presents a discrete particle swarm optimization (DPSO) algorithm with
heterogeneous (non-uniform) parameter values for solving the dynamic traveling salesman …
heterogeneous (non-uniform) parameter values for solving the dynamic traveling salesman …
Multi-objective-based radiomic feature selection for lesion malignancy classification
Objective: accurately classifying the malignancy of lesions detected in a screening scan is
critical for reducing false positives. Radiomics holds great potential to differentiate malignant …
critical for reducing false positives. Radiomics holds great potential to differentiate malignant …
A similarity-based multiobjective evolutionary algorithm for deployment optimization of near space communication system
The deployment of the airships plays a key role in maximizing the performance of the near
space communication system. The main problem is how to strike a balance between the …
space communication system. The main problem is how to strike a balance between the …
A hypervolume distribution entropy guided computation resource allocation mechanism for the multiobjective evolutionary algorithm based on decomposition
The computation resource allocation is a key issue to the multiobjective evolutionary
algorithms. Present studies still have some difficulties addressing this issue such as low …
algorithms. Present studies still have some difficulties addressing this issue such as low …