@InProceedings{10.1007/978-3-030-72062-9_9,
author="Ghosh, Abhiroop
and Deb, Kalyanmoy
and Averill, Ronald
and Goodman, Erik",
editor="Ishibuchi, Hisao
and Zhang, Qingfu
and Cheng, Ran
and Li, Ke
and Li, Hui
and Wang, Handing
and Zhou, Aimin",
title="Combining User Knowledge and Online Innovization for Faster Solution to Multi-objective Design Optimization Problems",
booktitle="Evolutionary Multi-Criterion Optimization",
year="2021",
publisher="Springer International Publishing",
address="Cham",
pages="102--114",
abstract="Real-world optimization problems often come with additional user knowledge that, when accommodated within an algorithm, may produce a faster convergence to acceptable solutions. Modern population-based optimization algorithms, aided by recent advances in AI and machine learning, can also learn and utilize patterns of variables from past iterations to improve convergence in subsequent iterations -- an approach termed innovization. In this paper, we discuss ways to combine user-supplied heuristics and machine-learnable patterns and rule sets in developing efficient multi-objective optimization algorithms. Two practical large-scale design problems are chosen to demonstrate the usefulness of integrating human-machine information within a multi-objective optimization in finding similar quality solutions as that obtained by the original algorithm with less computational time.",
isbn="978-3-030-72062-9"
}