ACM Computing Surveys (CSUR)
Heuristics for the Phylogeny Problem
Journal of Heuristics
Inferring Phylogenetic Trees Using Evolutionary Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
TreeRank: a similarity measure for nearest neighbor searching in phylogenetic database
SSDBM '03 Proceedings of the 15th International Conference on Scientific and Statistical Database Management
Estimating the destructiveness of crossover on binary tree representations
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Progressive Tree Neighborhood Applied to the Maximum Parsimony Problem
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
On the application of evolutionary algorithms to the consensus tree problem
EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
A unified view on hybrid metaheuristics
HM'06 Proceedings of the Third international conference on Hybrid Metaheuristics
A taxonomy of cooperative search algorithms
HM'05 Proceedings of the Second international conference on Hybrid Metaheuristics
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The consensus tree problem arises in the domain of phylogenetics and seeks to find for a given collection of trees a single tree best representing it. Usually, such a tree collection is obtained by biologists for a given taxa set either via different phylogenetic inference methods or multiple applications of a non-deterministic procedure. There exist various consensus methods which often have the drawback of being very strict, limiting the resulting consensus tree in terms of its resolution and/or precision. A reason for this typically is the coarse granularity of the tree metric used. To find fully resolved (binary) consensus trees of high quality, we consider the fine-grained TreeRank similarity measure and extend a previously presented evolutionary algorithm (EA) to a memetic algorithm (MA) by including different variants of local search using neighborhoods based on moves of single taxa as well as subtrees. Furthermore, we propose a variable neighborhood search (VNS) with an embedded variable neighborhood descent (VND) based on the same neighborhood structures. Finally sequential and intertwined combinations of the EA and MA with the VNS/VND are investigated. We give results on real and artificially generated data indicating in particular the benefits of the hybrid methods.