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

Authors:
Rajeev Kumar;Bipul Kumar Bal;Peter I. Rockett
Affiliations:
Indian Institute of Technology Kharagpur, Kharagpur, India;Indian Institute of Technology Kharagpur, Kharagpur, India;University of Sheffield, Sheffield S1 3JD, England, UK
Venue:
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Year:
2009

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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.