RT Conference Proceedings
T1 Phylogenetic inference's algorithms
A1 Fernández-Rovira, Alicia
A1 Gómez Jáuregui, Alvaro
A2 Biología Molecular y Bioquímica, 
K1 Algoritmos
K1 Filogenia
AB Phylogenetic inference consist in the search of an evolutionary tree to explain the best waypossible genealogical relationships of a set of species. Phylogenetic analysis has a large numberof applications in areas such as biology, ecology, paleontology, etc.There are several criterias which has been defined in order to infer phylogenies, among whichare the maximum parsimony and maximum likelihood. The first one tries to find thephylogenetic tree that minimizes the number of evolutionary steps needed to describe theevolutionary history among species, while the second tries to find the tree that has the highestprobability of produce the observed data according to an evolutionary model. The search of aphylogenetic tree can be formulated as a multi-objective optimization problem, which aims tofind trees which satisfy simultaneously (and as much as possible) both criteria of parsimony andlikelihood. Due to the fact that these criteria are different there won't be a single optimalsolution (a single tree), but a set of compromise solutions. The solutions of this set are called"Pareto Optimal".To find this solutions, evolutionary algorithms are being used with success nowadays.Thisalgorithms are a family of techniques, which aren’t exact, inspired by the process of naturalselection. They usually find great quality solutions in order to resolve convoluted optimizationproblems. The way this algorithms works is based on the handling of a set of trial solutions (treesin the phylogeny case) using operators, some of them exchanges information between solutions,simulating DNA crossing, and others apply aleatory modifications, simulating a mutation. Theresult of this algorithms is an approximation to the set of the “Pareto Optimal” which can beshown in a graph with in order that the expert in the problem (the biologist when we talk aboutinference) can choose the solution of the commitment which produces the higher interest.In the case of optimization multi-objective applied to phylogenetic inference, there is opensource software tool, called MO-Phylogenetics, which is designed for the purpose of resolvinginference problems with classic evolutionary algorithms and last generation algorithms.REFERENCES[1] C.A. Coello Coello, G.B. Lamont, D.A. van Veldhuizen. Evolutionary algorithms for solvingmulti-objective problems. Spring. Agosto 2007[2] C. Zambrano-Vega, A.J. Nebro, J.F Aldana-Montes. MO-Phylogenetics: a phylogeneticinference software tool with multi-objective evolutionary metaheuristics. Methods in Ecologyand Evolution. En prensa. Febrero 2016.
YR 2016
FD 2016-06-07
LK http://hdl.handle.net/10630/11577
UL http://hdl.handle.net/10630/11577
LA eng
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026