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Erschienen in:
Maximum Likelihood Estimation Based on Random Subspace EDA: Application to Extrasolar Planet Detection Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Constrained Dimensionally Aware Genetic Programming for Evolving Interpretable Dispatching Rules in Dynamic Job Shop Scheduling

verfasst von : Yi Mei, Su Nguyen, Mengjie Zhang

Erschienen in: Simulated Evolution and Learning

Verlag: Springer International Publishing

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Abstract

This paper investigates the interpretability of the Genetic Programming (GP)-evolved dispatching rules for dynamic job shop scheduling problems. We incorporate the physical dimension of the features used in the terminal set of GP, and assume that the rules that aggregate the features with the same physical dimension are more interpretable. Based on this assumption, we define a new interpretability measure called dimension gap, and develop a Constrained Dimensionally Aware GP (C-DAGP) that optimises the effectiveness and interpretability simultaneously. In C-DAGP, the fitness is defined as a penalty function with a newly proposed penalty coefficient adaptation scheme. The experimental results show that the proposed C-DAGP can achieve better tradeoff between effectiveness and interpretability compared against the baseline GP and an existing DAGP.

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Vorheriges Kapitel Geometric Semantic Genetic Programming with Perpendicular Crossover and Random Segment Mutation for Symbolic Regression
Nächstes Kapitel Visualisation and Optimisation of Learning Classifier Systems for Multiple Domain Learning
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Metadaten
Titel
Constrained Dimensionally Aware Genetic Programming for Evolving Interpretable Dispatching Rules in Dynamic Job Shop Scheduling
verfasst von
Yi Mei
Su Nguyen
Mengjie Zhang
Copyright-Jahr
2017
Buch
Simulated Evolution and Learning

Print ISBN: 978-3-319-68758-2

Electronic ISBN: 978-3-319-68759-9

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-68759-9_36