136,365 research outputs found
Phylogenetic inference's algorithms
Phylogenetic inference consist in the search of an evolutionary tree to explain the best way
possible genealogical relationships of a set of species. Phylogenetic analysis has a large number
of applications in areas such as biology, ecology, paleontology, etc.
There are several criterias which has been defined in order to infer phylogenies, among which
are the maximum parsimony and maximum likelihood. The first one tries to find the
phylogenetic tree that minimizes the number of evolutionary steps needed to describe the
evolutionary history among species, while the second tries to find the tree that has the highest
probability of produce the observed data according to an evolutionary model. The search of a
phylogenetic tree can be formulated as a multi-objective optimization problem, which aims to
find trees which satisfy simultaneously (and as much as possible) both criteria of parsimony and
likelihood. Due to the fact that these criteria are different there won't be a single optimal
solution (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.This
algorithms are a family of techniques, which aren’t exact, inspired by the process of natural
selection. They usually find great quality solutions in order to resolve convoluted optimization
problems. The way this algorithms works is based on the handling of a set of trial solutions (trees
in the phylogeny case) using operators, some of them exchanges information between solutions,
simulating DNA crossing, and others apply aleatory modifications, simulating a mutation. The
result of this algorithms is an approximation to the set of the “Pareto Optimal” which can be
shown in a graph with in order that the expert in the problem (the biologist when we talk about
inference) 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 open
source software tool, called MO-Phylogenetics, which is designed for the purpose of resolving
inference 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 solving
multi-objective problems. Spring. Agosto 2007
[2] C. Zambrano-Vega, A.J. Nebro, J.F Aldana-Montes. MO-Phylogenetics: a phylogenetic
inference software tool with multi-objective evolutionary metaheuristics. Methods in Ecology
and Evolution. En prensa. Febrero 2016
Multi-concentric optimal charging cordon design
The performance of a road pricing scheme varies greatly by its actual design and implementation. The design
of the scheme is also normally constrained by several practicality requirements. One of the practicality
requirements which is tackled in this paper is the topology of the charging scheme. The cordon shape of the
pricing scheme is preferred due to its user-friendliness (i.e. the scheme can be understood easily). This has
been the design concept for several real world cases (e.g. the schemes in London, Singapore, and Norway).
The paper develops a methodology for defining an optimal location of a multi-concentric charging cordons
scheme using Genetic Algorithm (GA). The branch-tree structure is developed to represent a valid charging
cordon scheme which can be coded using two strings of node numbers and number of descend nodes. This
branch-tree structure for a single cordon is then extended to the case with multi-concentric charging cordons.
GA is then used to evolve the design of a multi-concentric charging cordons scheme encapsulated in the twostring
chromosome. The algorithm developed, called GA-AS, is then tested with the network of the Edinburgh
city in UK. The results suggest substantial improvements of the benefit from the optimised charging cordon
schemes as compared to the judgemental ones which illustrate the potential of this algorithm
MAA*: A Heuristic Search Algorithm for Solving Decentralized POMDPs
We present multi-agent A* (MAA*), the first complete and optimal heuristic
search algorithm for solving decentralized partially-observable Markov decision
problems (DEC-POMDPs) with finite horizon. The algorithm is suitable for
computing optimal plans for a cooperative group of agents that operate in a
stochastic environment such as multirobot coordination, network traffic
control, `or distributed resource allocation. Solving such problems efiectively
is a major challenge in the area of planning under uncertainty. Our solution is
based on a synthesis of classical heuristic search and decentralized control
theory. Experimental results show that MAA* has significant advantages. We
introduce an anytime variant of MAA* and conclude with a discussion of
promising extensions such as an approach to solving infinite horizon problems.Comment: Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005
Learning optimization models in the presence of unknown relations
In a sequential auction with multiple bidding agents, it is highly
challenging to determine the ordering of the items to sell in order to maximize
the revenue due to the fact that the autonomy and private information of the
agents heavily influence the outcome of the auction.
The main contribution of this paper is two-fold. First, we demonstrate how to
apply machine learning techniques to solve the optimal ordering problem in
sequential auctions. We learn regression models from historical auctions, which
are subsequently used to predict the expected value of orderings for new
auctions. Given the learned models, we propose two types of optimization
methods: a black-box best-first search approach, and a novel white-box approach
that maps learned models to integer linear programs (ILP) which can then be
solved by any ILP-solver. Although the studied auction design problem is hard,
our proposed optimization methods obtain good orderings with high revenues.
Our second main contribution is the insight that the internal structure of
regression models can be efficiently evaluated inside an ILP solver for
optimization purposes. To this end, we provide efficient encodings of
regression trees and linear regression models as ILP constraints. This new way
of using learned models for optimization is promising. As the experimental
results show, it significantly outperforms the black-box best-first search in
nearly all settings.Comment: 37 pages. Working pape
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