On distances between phylogenetic trees
SODA '97 Proceedings of the eighth annual ACM-SIAM symposium on Discrete algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Omni-aiNet: an immune-inspired approach for omni optimization
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
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
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
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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This work presents the application of the omni-aiNet algorithm - an immune-inspired algorithm originally developed to solve single and multiobjective optimization problems - to the construction of phylogenetic trees. The main goal of this work is to automatically evolve a population of phylogenetic unrooted trees, possibly with distinct topologies, by minimizing at the same time the minimal evolution and the mean-squared error criteria. The obtained set of phylogenetic trees contains non-dominated individuals that form the Pareto front and that represent the trade-off of the two conflicting objectives. The proposal of multiple non-dominated solutions in a single run gives to the user the possibility of having distinct explanations for the difference observed in the terminal nodes of the tree, and also indicates the restrictive feedback provided by the individual application of well-known algorithms for phylogenetic reconstruction that takes into account both optimization criteria, like Neighbor Joining.