Parallel phylogenetic inference
Proceedings of the 2000 ACM/IEEE conference on Supercomputing
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Inferring Phylogenetic Trees Using Evolutionary Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Gaphyl: An Evolutionary Algorithms Approach For The Study Of Natural Evolution
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Multi-objective evolutionary algorithms and phylogenetic inference with multiple data sets
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Progressive Tree Neighborhood Applied to the Maximum Parsimony Problem
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Evolving phylogenetic trees: a multiobjective approach
BSB'07 Proceedings of the 2nd Brazilian conference on Advances in bioinformatics and computational biology
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Inferring phylogenetic trees using a multiobjective artificial bee colony algorithm
EvoBIO'12 Proceedings of the 10th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
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Maximum parsimony and maximum likelihood approaches to phylogenetic reconstruction were proposed with the aim of describing the evolutionary history of species by using different optimality principles. These discrepant points of view can lead to situations where discordant topologies are inferred from a same dataset. In recent years, research efforts in Phylogenetics try to apply multiobjective optimization techniques to generate phylogenetic topologies which suppose a consensus among different criteria. In order to generate high quality topologies, it is necessary to perform an exhaustive study about topological search strategies as well as to decide the most fitting molecular evolutionary model in agreement with statistical measurements. In this paper we report a study on different operators and models to improve a Multiobjective Artificial Bee Colony algorithm for inferring phylogenies according to the parsimony and likelihood criteria. Experimental results have been evaluated using the hypervolume metrics and compared with other multiobjective proposals and state-of-the-art phylogenetic software.