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Biology of the Physarum polycephalum Plasmodium: Preliminaries for Unconventional Computing Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Physarum Learner: A Slime Mold Inspired Structural Learning Approach

Authors : T. Schön, M. Stetter, O. Belova, A. Koch, A. M. Tomé, E. W. Lang

Published in: Advances in Physarum Machines

Publisher: Springer International Publishing

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Abstract

A novel Score-based Physarum Learner algorithm for learning Bayesian Network structure from data is introduced and shown to outperform common score based structure learning algorithms for some benchmark data sets. The Score-based Physarum Learner first initializes a fully connected Physarum-Maze with random conductances. In each Physarum Solver iteration, the source and sink nodes are changed randomly, and the conductances are updated. Connections exceeding a predefined conductance threshold are considered as Bayesian Network edges, and the score of the connected nodes are examined in both directions. A positive or negative feedback is given to the edge conductance based on the calculated scores. Due to randomness in selecting connections for evaluation, an ensemble of Score-based Physarum Learner is used to build the final Bayesian Network structure.

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previous chapter Modelling Oscillatory Behaviour of Slime Mould
next chapter Slime Mould Inspired Applications on Graph-Optimization Problems
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Metadata
Title
Physarum Learner: A Slime Mold Inspired Structural Learning Approach
Authors
T. Schön
M. Stetter
O. Belova
A. Koch
A. M. Tomé
E. W. Lang
Copyright Year
2016
Book
Advances in Physarum Machines

Print ISBN: 978-3-319-26661-9

Electronic ISBN: 978-3-319-26662-6

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-26662-6_25

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