This is based on survived writings.
Phylogeny, Optimisation, Heuristic methods, Simulated annealing, Phase transition, Search landscape Abstract Phylogeny reconstruction with global criteria is NP-complete or NP-hard, hence in general requires a heuristic search.
We investigate the powerful, physically inspired, general-purpose heuristic simulated annealing, applied to phylogeny reconstruction. Simulated annealing mimics the physical process of annealing, where a liquid is gently cooled to form a crystal.
During the search, periods of elevated specific heat occur, analogous to physical phase transitions. These simulated annealing phase transitions play a crucial role in the outcome of the search.
Nevertheless, they have received comparably little attention, for phylogeny or other optimisation problems. We analyse simulated annealing phase transitions during searches for the optimal phylogenetic tree for 34 real-world multiple alignments.
In the same way in which melting temperatures differ between materials, we observe distinct specific heat profiles for each input file. We discuss application in algorithmic optimisation and as a diagnostic to assess parameterisation before computationally costly, large phylogeny reconstructions are launched.
Whilst the focus here lies on phylogeny reconstruction under maximum parsimony, it is plausible that our results are more widely applicable to optimisation procedures in science and industry.
Introduction Global optimisation is an important step in modern phylogeny reconstruction. To identify the most plausible phylogenetic tree under a given criterion, one has to find the tree with optimal fit amongst all conceivable tree topologies.
The maximum parsimony criterion MP requires optimisation of tree length Fitch, and maximum likelihood MLas the name suggests, requires optimisation of a likelihood function Felsenstein, It follows that in order to be sure of obtaining the optimal tree, one would theoretically need to examine all feasible topologies; a number which grows factorially with the number of taxonomic units in the analysis Felsenstein, a.
Even for studies of moderate size, this number exceeds the number of atoms in the universe and makes an exhaustive search infeasible.
Despite reductions in complexity achieved by the branch-and-bound algorithm Hendy and Penny, and revolutions in computing power, it is likely that run time for exact optimisation will remain astronomical. As a workaround, heuristic optimisation algorithms are used. Instead of setting out to determine all globally optimal solutions, these methods use shortcuts to aim to find optima approximately.
As such, heuristics allow one to obtain very good solutions in practical time scales when exact optimisation is too costly.
This is also true in phylogeny reconstruction. However, in emplyoing heuristic searches we fundamentally lose the guarantee of global optimality. Using a heuristic search it remains possible that even if the true phylogeny was hypothetically inferable from an alignment, one might not be able to retrieve it — simply because the algorithm did not happen to search a specific area of the search space.
This problem is particularly pressing for analyses involving many taxonomic units. The increasing ease and decreasing costs of DNA sequencing allow opportunities for very large phylogeny reconstructions.
At the same time, phylogeny is being applied to even more areas of the life sciences. Applications include, for example, ancestral state reconstruction Lutzoni et al. Such analyses build on the solution of large and complex optimisation problems, making further development of heuristic searches in phylogeny increasingly urgent.
An important step towards more efficient algorithms is a better understanding of the nature of heuristic searches.Abstract. Dental caries is an infectious disease that causes tooth decay. The high prevalence of dental caries in recent humans is attributed to more frequent consumption of plant foods rich in fermentable carbohydrates in food-producing societies.
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