Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
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
Multiobjective Optimization in Bioinformatics and Computational Biology
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Journal of Global Optimization
A multi-objective evolutionary approach for phylogenetic inference
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
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
IEEE Transactions on Evolutionary Computation
TPNC'12 Proceedings of the First international conference on Theory and Practice of Natural Computing
A multiobjective proposal based on the firefly algorithm for inferring phylogenies
EvoBIO'13 Proceedings of the 11th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
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Phylogenetic Inference is considered as one of the most important research topics in the field of Bioinformatics. A variety of methods based on different optimality measures has been proposed in order to build and evaluate the trees which describe the evolution of species. A major problem that arises with this kind of techniques is the possibility of inferring discordant topologies from a same dataset. Another question to be resolved is how to manage the tree search process. As the space of possible topologies increases exponentially with the number of species in the input dataset, exhaustive methods cannot be applied. In this paper we propose a multiobjective adaptation of a well-known Swarm Intelligence algorithm, the Artificial Bee Colony, to reconstruct phylogenetic trees according to two criteria: maximum parsimony and maximum likelihood. Our approach shows a significant improvement in the quality of the inferred trees compared to other multiobjective proposals.