NDLI: Multiobjective genetic programming approach to evolving heuristics for the bounded diameter minimum spanning tree problem: MOGP for BDMST

Content Provider	ACM Digital Library
Author	Rockett, Peter I. Bal, Bipul Kumar Kumar, Rajeev
Abstract	The bounded-diameter (or diameter-constrained) minimum spanning tree (BDMST) problem is a well-studied combinatorial optimization problem for which several heuristics such as: one-time tree construction (OTTC), center based tree construction(CBTC), iterative refinement (IR) and randomized greedy heuristics (RGH) have been developed. Very little work, however, has been done on producing heuristics for BDMSTs using evolutionary algorithms. In this paper we have used multiobjective genetic programming (MOGP) to explore the evolution of a set of heuristics for the BDMST problem. The quality of the Pareto fronts obtained from the MOGP-evolved heuristics is comparable to, or in some cases better than, the Pareto fronts generated by established, hand-crafted heuristics. MOGP is thus a very promising technique for finding heuristics for BDMSTs and more general multiobjective combinatorial optimizations.
Starting Page	309
Ending Page	316
Page Count	8
File Format	PDF
ISBN	9781605583259
DOI	10.1145/1569901.1569945
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-07-08
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Pareto front Genetic algorithm Genetic programming Heuristics Multiobjective optimization Optimization methods Bloat Combinatorial optimization
Content Type	Text
Resource Type	Article

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Multiobjective genetic programming approach to evolving heuristics for the bounded diameter minimum spanning tree problem: MOGP for BDMST