Phenotype Based Surrogate-Assisted Multi-objective Genetic Programming with Brood Recombination for Dynamic Flexible Job Shop Scheduling
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{Zhang:2022:SSCI,
-
author = "Fangfang Zhang and Yi Mei and Su Nguyen and
Mengjie Zhang",
-
title = "Phenotype Based Surrogate-Assisted Multi-objective
Genetic Programming with Brood Recombination for
Dynamic Flexible Job Shop Scheduling",
-
booktitle = "2022 IEEE Symposium Series on Computational
Intelligence (SSCI)",
-
year = "2022",
-
pages = "1218--1225",
-
abstract = "Dynamic flexible job shop scheduling (DFJSS) is an
important combinatorial optimisation problem with a
large number of real-world applications such as
component production in manufacturing. Genetic
programming (GP), as a hyper-heuristic approach, has
been widely used to learn scheduling heuristics for
DFJSS. Brood recombination has been shown its
effectiveness to improve the performance of GP by
generating more offspring and preselecting only
promising individuals into the next generation.
However, evaluating more individuals requires more
computational cost. Phenotype based surrogate models
have been successfully used with GP to speed up the
evaluation in single-objective dynamic job shop
scheduling. However, its effectiveness on
multi-objective dynamic job shop scheduling is unknown.
To fill this gap, this paper proposes a novel
surrogate-assisted multi-objective GP based on the
phenotype of GP individuals for DFJSS. Specifically, we
first use phenotypic vector to represent the behaviour
of GP individuals in DFJSS. Second, K-nearest neighbour
based surrogates are built according to the phenotypic
characterisations and multiple fitness values of the
evaluated individuals. Last, the built surrogate models
are used to predict the fitness of newly generated
offspring in GP with brood recombination. The results
show that with the same training time, the proposed
algorithm can achieve significantly better scheduling
heuristics than the compared algorithm. The analyses of
population diversity, feature importance, and the
number of non-dominated individuals have also shown the
effectiveness of the proposed algorithm in different
aspects.",
-
keywords = "genetic algorithms, genetic programming",
-
DOI = "doi:10.1109/SSCI51031.2022.10022169",
-
month = dec,
-
notes = "Also known as \cite{10022169}",
- }
Genetic Programming entries for
Fangfang Zhang
Yi Mei
Su Nguyen
Mengjie Zhang
Citations