A Fitness-based Selection Method for Pareto Local Search for Many-Objective Job Shop Scheduling
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Masood:2020:CEC,
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author = "A. Masood and G. Chen and Y. Mei and H. Al-Sahaf and
M. Zhang",
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title = "A Fitness-based Selection Method for {Pareto} Local
Search for Many-Objective Job Shop Scheduling",
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booktitle = "2020 IEEE Congress on Evolutionary Computation (CEC)",
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year = "2020",
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editor = "Yaochu Jin",
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month = "19-24 " # jul,
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keywords = "genetic algorithms, genetic programming, Dispatching,
Job shop scheduling, Optimisation, Search problems,
Schedules, Sociology, Statistics",
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isbn13 = "978-1-7281-6929-3",
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DOI = "doi:10.1109/CEC48606.2020.9185881",
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abstract = "Genetic programming (GP) is considered the most
popular method for automatically discovering and
constructing dispatching rules for scheduling problems.
Pareto Local Search (PLS) is a simple and effective
local search method for tackling multi-objective
combinatorial optimization problems. Researchers have
studied the application of PLS to multiobjective
evolutionary algorithms (MOEAs) with some success. In
fact, by hybridizing global search with local search,
the performance of many MOEAs can be noticeably
improved. Despite its preliminary success, the
practical use of PLS in GP is relatively limited. In
this study, our aim is to enhance the quality of
evolved dispatching rules for many-objective Job Shop
Scheduling (JSS) through hybridizing GP with PLS
techniques and designing an effective selection
mechanism of initial solutions for PLS. In this paper,
we propose a new GP-PLS algorithm that investigates
whether the fitness-based selection mechanism for
selecting initial solutions for PLS can increase the
chance of discovering highly effective dispatching
rules for many-objective JSS. To evaluate the
effectiveness of our new algorithm, GPPLS is compared
with the current state-of-the-art algorithms for
many-objective JSS. The experimental results confirm
that the proposed method can outperform the four
recently proposed algorithms because of the proper use
of local search techniques.",
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notes = "Also known as \cite{9185881}",
- }
Genetic Programming entries for
Atiya Masood
G Chen
Y Mei
Harith Al-Sahaf
Mengjie Zhang
Citations