A New Evolutionary Approach to the Degree Constrained Minimum Spanning Tree Problem

A New Evolutionary Approach to the Degree Constrained Minimum Spanning Tree Problem (2000)

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by Joshua Knowles , David Corne , Martin Oates

Venue:	IEEE Transactions on Evolutionary Computation
Citations:	11 - 0 self

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Datum	Value	Source
TITLE	A New Evolutionary Approach to the Degree Constrained Minimum Spanning Tree Problem	user correction - Legacy Corrections
AUTHOR NAME	Joshua Knowles	SVM HeaderParse 0.1
AUTHOR AFFIL	Department of Computer Science	SVM HeaderParse 0.2
AUTHOR ADDR	University of Reading, UK	SVM HeaderParse 0.1
AUTHOR NAME	David Corne	SVM HeaderParse 0.1
AUTHOR AFFIL	Department of Computer Science	SVM HeaderParse 0.2
AUTHOR ADDR	University of Reading, UK	SVM HeaderParse 0.1
AUTHOR NAME	Martin Oates	SVM HeaderParse 0.1
AUTHOR AFFIL	Department of Computer Science	SVM HeaderParse 0.2
AUTHOR ADDR	University of Reading, UK	SVM HeaderParse 0.1
ABSTRACT	Finding the degree-constrained minimum spanning tree (d-MST) of a graph is a well studied NP-hard problem which is important in network design. We introduce a new method which improves on the best technique previously published for solving the d-MST, either using heuristic or evolutionary approaches. The basis of this encoding is a spanning-tree construction algorithm which we call the Randomised Primal Method (RPM), based on the well-known Prim's algorithm [6], and an extension [4] which we call `d-Prim's'. We describe a novel encoding for spanning trees, which involves using the RPM to interpret lists of potential edges to include in the growing tree. We also describe a random graph generator which produces particularly challenging d-MST problems. On these and other problems, we find that an evolutionary algorithm (EA) using the RPM encoding outperforms the previous best published technique from the operations research literature, and also outperforms simulated...	user correction - Legacy Corrections
YEAR	2000	INFERENCE
VENUE	IEEE Transactions on Evolutionary Computation	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	125--134	INFERENCE
VOLUME	4	INFERENCE
CITATIONS	13 found	ParsCit 1.0