A stochastic local search approach to language tree reconstruction

2012 ◽  
pp. 155-172
Author(s):  
Francesca Tria ◽  
Emanuele Caglioti ◽  
Vittorio Loreto ◽  
Andrea Pagnani
Keyword(s):  
Local Search ◽  
Stochastic Local Search ◽  
Tree Reconstruction ◽  
Search Approach

A stochastic local search approach to language tree reconstruction

Diachronica ◽  
2010 ◽  
Vol 27 (2) ◽  
pp. 341-358 ◽  
Author(s):  
Francesca Tria ◽  
Emanuele Caglioti ◽  
Vittorio Loreto ◽  
Andrea Pagnani
Keyword(s):  
Local Search ◽  
Phylogenetic Trees ◽  
Search Algorithm ◽  
Distance Matrix ◽  
Stochastic Local Search ◽  
Strongly Correlated ◽  
The Stability ◽  
Search Approach

In this paper we introduce a novel stochastic local search algorithm to reconstruct phylogenetic trees. We focus in particular on the reconstruction of language trees based on the comparison of the Swadesh lists of the recently compiled ASJP database. Starting from a generic tree configuration, our scheme stochastically explores the space of possible trees driven by the minimization of a pseudo-functional quantifying the violations of additivity of the distance matrix. As a consequence the resulting tree can be annotated with the values of the violations on each internal branch. The values of the deviations are strongly correlated with the stability of the internal edges; they are measured with a novel bootstrap procedure and displayed on the tree as an additional annotation. As a case study we considered the reconstruction of the Indo-European language tree. The results are quite encouraging, highlighting a potential new avenue to investigate the role of the deviations from additivity and check the reliability and consistency of the reconstructed trees.

Stochastic local search with learning automaton for the swap-body vehicle routing problem

2018 ◽  
Vol 89 ◽  
pp. 68-81 ◽  
Author(s):  
Túlio A.M. Toffolo ◽  
Jan Christiaens ◽  
Sam Van Malderen ◽  
Tony Wauters ◽  
Greet Vanden Berghe
Keyword(s):  
Local Search ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Stochastic Local Search ◽  
Routing Problem ◽  
Learning Automaton

scholarly journals Circumspect descent prevails in solving random constraint satisfaction problems

2008 ◽  
Vol 105 (40) ◽  
pp. 15253-15257 ◽  
Author(s):  
Mikko Alava ◽  
John Ardelius ◽  
Erik Aurell ◽  
Petteri Kaski ◽  
Supriya Krishnamurthy ◽  
...  
Keyword(s):  
Local Search ◽  
Linear Time ◽  
Search Algorithm ◽  
Solution Space ◽  
Search Algorithms ◽  
Constraint Satisfaction Problems ◽  
Problem Instance ◽  
Stochastic Local Search ◽  
Local Search Algorithms ◽  
Local Energy Minimum

We study the performance of stochastic local search algorithms for random instances of the K-satisfiability (K-SAT) problem. We present a stochastic local search algorithm, ChainSAT, which moves in the energy landscape of a problem instance by never going upwards in energy. ChainSAT is a focused algorithm in the sense that it focuses on variables occurring in unsatisfied clauses. We show by extensive numerical investigations that ChainSAT and other focused algorithms solve large K-SAT instances almost surely in linear time, up to high clause-to-variable ratios α; for example, for K = 4 we observe linear-time performance well beyond the recently postulated clustering and condensation transitions in the solution space. The performance of ChainSAT is a surprise given that by design the algorithm gets trapped into the first local energy minimum it encounters, yet no such minima are encountered. We also study the geometry of the solution space as accessed by stochastic local search algorithms.

An FPGA Solver for Partial MaxSAT Problems Based on Stochastic Local Search

2017 ◽  
Vol 44 (4) ◽  
pp. 32-37
Author(s):  
Shohei Sassa ◽  
Kenji Kanazawa ◽  
Shaowei Cai ◽  
Moritoshi Yasunaga
Keyword(s):  
Local Search ◽  
Stochastic Local Search
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