A genetic algorithm for flowshop sequencing
Computers and Operations Research - Special issue on genetic algorithms
An approach to a problem in network design using genetic algorithms
An approach to a problem in network design using genetic algorithms
SAC '94 Proceedings of the 1994 ACM symposium on Applied computing
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Introduction to Algorithms
Network random keys: a tree representation scheme for genetic and evolutionary algorithms
Evolutionary Computation
Proceedings of the 2003 ACM symposium on Applied computing
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Representations for Genetic and Evolutionary Algorithms
Representations for Genetic and Evolutionary Algorithms
Improved heuristics for the bounded-diameter minimum spanning tree problem
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A new evolutionary approach to the degree-constrained minimumspanning tree problem
IEEE Transactions on Evolutionary Computation
Edge sets: an effective evolutionary coding of spanning trees
IEEE Transactions on Evolutionary Computation
Analysis of properties of recombination operators proposed for the node-depth encoding
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
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The node-depth encoding has elements from direct and indirect encoding for trees which encodes trees by storing the depth of nodes in a list. Node-depth encoding applies specific search operators that is a typical characteristic for direct encodings. An investigation into the bias of the initialization process and the mutation operators of the node-depth encoding shows that the initialization process has a bias to solutions with small depths and diameters, and a bias towards stars. This investigation, also, shows that the mutation operators are unbiased. The performance of node-depth encoding is investigated for the bounded-diameter minimum spanning tree problem. The results are presented for Euclidean instances presented in the literature. In contrast with the expectation, the evolutionary algorithm using the biased initialization operator does not allow evolutionary algorithms to find better solutions compared to an unbiased initialization. In comparison to other evolutionary algorithms for the bounded-diameter minimum spanning tree evolutionary algorithms using the node-depth encoding have a good performance.