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
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Multi-objective evolutionary algorithms and phylogenetic inference with multiple data sets
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Multiobjective Optimization in Bioinformatics and Computational Biology
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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
Firefly algorithm, stochastic test functions and design optimisation
International Journal of Bio-Inspired Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
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
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Recently, swarm intelligence algorithms have been applied successfully to a wide variety of optimization problems in Computational Biology. Phylogenetic inference represents one of the key research topics in this area. Throughout the years, controversy among biologists has arisen when dealing with this well-known problem, as different optimality criteria can give as a result discordant genealogical relationships. Current research efforts aim to apply multiobjective optimization techniques in order to infer phylogenies that represent a consensus between different principles. In this work, we apply a multiobjective swarm intelligence approach inspired by the behaviour of fireflies to tackle the phylogenetic inference problem according to two criteria: maximum parsimony and maximum likelihood. Experiments on four real nucleotide data sets show that this novel proposal can achieve promising results in comparison with other approaches from the state-of-the-art in Phylogenetics.