Paper Info
Title
Evolutionary Multitasking via Explicit Autoencoding.
Abstract
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 evolutionary search on multiple tasks simultaneously. It aims to improve convergence characteristics across multiple optimization problems at once by seamlessly transferring knowledge among them. Due to the efficacy of EMT, it has attracted lots of research attentions and several EMT algorithms have been proposed in the literature. However, existing EMT algorithms are usually based on a common mode of knowledge transfer in the form of implicit genetic transfer through chromosomal crossover. This mode cannot make use of multiple biases embedded in different evolutionary search operators, which could give better search performance when properly harnessed. Keeping this in mind, this paper proposes an EMT algorithm with explicit genetic transfer across tasks, namely EMT via autoencoding, which allows the incorporation of multiple search mechanisms with different biases in the EMT paradigm. To confirm the efficacy of the proposed EMT algorithm with explicit autoencoding, comprehensive empirical studies have been conducted on both the single- and multi-objective multitask optimization problems.
Year
DOI
Venue
2019
10.1109/TCYB.2018.2845361
IEEE transactions on cybernetics
Keywords
Field
DocType
Task analysis,Optimization,Genetics,Search problems,Sociology,Statistics,Multitasking
Convergence (routing),Autoencoder,Task analysis,Knowledge transfer,Evolutionary computation,Artificial intelligence,Human multitasking,Optimization problem,Empirical research,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
49
9
2168-2275
Citations 
PageRank 
References 
30
0.75
18
Authors
7
Name
Order
Citations
PageRank
Liang Feng160148.54
Lei Zhou211828.02
Jing-hui Zhong338033.00
Abhishek Gupta435120.59
Yew-Soon Ong54205224.11
Kay Chen Tan62767164.86
A. K. Qin73496146.50