Optimization, approximation, and complexity classes
STOC '88 Proceedings of the twentieth annual ACM symposium on Theory of computing
An approach to a problem in network design using genetic algorithms
An approach to a problem in network design using genetic algorithms
Approximation algorithms for some optimum communication spanning tree problems
Discrete Applied Mathematics
Weight-biased edge-crossover in evolutionary algorithms for two graph problems
Proceedings of the 2001 ACM symposium on Applied computing
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Network random keys: a tree representation scheme for genetic and evolutionary algorithms
Evolutionary Computation
Deterministic Polylog Approximation for Minimum Communication Spanning Trees
ICALP '98 Proceedings of the 25th International Colloquium on Automata, Languages and Programming
A New Evolutionary Approach for the Optimal Communication Spanning Tree Problem
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Representations for Genetic and Evolutionary Algorithms
Representations for Genetic and Evolutionary Algorithms
A memetic algorithm for the optimum communication spanning tree problem
HM'07 Proceedings of the 4th international conference on Hybrid metaheuristics
Edge sets: an effective evolutionary coding of spanning trees
IEEE Transactions on Evolutionary Computation
New insights into the OCST problem: integrating node degrees and their location in the graph
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
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The optimal communication spanning tree (OCST) problem is a well known $\mathcal{NP}$-hard combinatorial optimization problem which seeks a spanning tree that satisfies all given communication requirements for minimal total costs. It has been shown that optimal solutions of OCST problems are biased towards the much simpler minimum spanning tree (MST) problem. Therefore, problem-specific representations for EAs like heuristic variants of edge-sets that are biased towards MSTs show high performance. In this paper, additional properties of optimal solutions for Euclidean variants of OCST problems are studied. Experimental results show that not only edges in optimal trees are biased towards low-distance weights but also edges which are directed towards the graph's center are overrepresented in optimal solutions. Therefore, efficient heuristic search algorithms for OCST should be biased towards edges with low distance weight \emph{and} edges that point towards the center of the graph. Consequently, we extend the recombination operator of edge-sets such that the orientation of the edges is considered for constructing offspring solutions. Experimental results show a higher search performance in comparison to EAs using existing crossover strategies of edge-sets. As a result, we suggest to consider not only the distance weights but also the orientation of edges in heuristic solution approaches for the OCST problem.