Heuristics for the Phylogeny Problem
Journal of Heuristics
On contract-and-refine transformations between phylogenetic trees
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
A distance-based information preservation tree crossover for the maximum parsimony problem
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Local search for the maximum parsimony problem
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
An efficient probabilistic population-based descent for the median genome problem
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Finding consensus trees by evolutionary, variable neighborhood search, and hybrid algorithms
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A Memetic Algorithm for Phylogenetic Reconstruction with Maximum Parsimony
EvoBIO '09 Proceedings of the 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Neighborhood analysis: a case study on curriculum-based course timetabling
Journal of Heuristics
A distance-based information preservation tree crossover for the maximum parsimony problem
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Proceedings of the 14th annual conference on Genetic and 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
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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The Maximum Parsimony problem aims at reconstructing a phylogenetic tree from DNA sequences while minimizing the number of genetic transformations. To solve this NP-complete problem, heuristic methods have been developed, often based on local search. In this article, we focus on the influence of the neighborhood relations. After analyzing the advantages and drawbacks of the well-known NNI, SPR and TBR neighborhoods, we introduce the concept of Progressive Neighborhood which consists in constraining progressively the size of the neighborhood as the search advances. We empirically show that applied to the Maximum Parsimony problem, this progressive neighborhood turns out to be more efficient and robust than the classic neighborhoods using a descent algorithm. Indeed, it allows to find better solutions with a smaller number of iterations or trees evaluated.