1,782,324 research outputs found
Recommended from our members
Local search: A guide for the information retrieval practitioner
There are a number of combinatorial optimisation problems in information retrieval in which the use of local search methods are worthwhile. The purpose of this paper is to show how local search can be used to solve some well known tasks in information retrieval (IR), how previous research in the field is piecemeal, bereft of a structure and methodologically flawed, and to suggest more rigorous ways of applying local search methods to solve IR problems. We provide a query based taxonomy for analysing the use of local search in IR tasks and an overview of issues such as fitness functions, statistical significance and test collections when conducting experiments on combinatorial optimisation problems. The paper gives a guide on the pitfalls and problems for IR practitioners who wish to use local search to solve their research issues, and gives practical advice on the use of such methods. The query based taxonomy is a novel structure which can be used by the IR practitioner in order to examine the use of local search in IR
Searching for Bayesian Network Structures in the Space of Restricted Acyclic Partially Directed Graphs
Although many algorithms have been designed to construct Bayesian network
structures using different approaches and principles, they all employ only two
methods: those based on independence criteria, and those based on a scoring
function and a search procedure (although some methods combine the two). Within
the score+search paradigm, the dominant approach uses local search methods in
the space of directed acyclic graphs (DAGs), where the usual choices for
defining the elementary modifications (local changes) that can be applied are
arc addition, arc deletion, and arc reversal. In this paper, we propose a new
local search method that uses a different search space, and which takes account
of the concept of equivalence between network structures: restricted acyclic
partially directed graphs (RPDAGs). In this way, the number of different
configurations of the search space is reduced, thus improving efficiency.
Moreover, although the final result must necessarily be a local optimum given
the nature of the search method, the topology of the new search space, which
avoids making early decisions about the directions of the arcs, may help to
find better local optima than those obtained by searching in the DAG space.
Detailed results of the evaluation of the proposed search method on several
test problems, including the well-known Alarm Monitoring System, are also
presented
GLOBAL OPTIMIZATION METHODS
Training a neural network is a difficult optimization problem because of numerous local minimums. Many global search algorithms have been used to train neural networks. However, local search algorithms are more efficient with computational resources, and therefore numerous random restarts with a local algorithm may be more effective than a global algorithm. This study uses Monte-Carlo simulations to determine the relative efficiency of a local search algorithm to 9 stochastic global algorithms. The computational requirements of the global algorithms are several times higher than the local algorithm and there is little gain in using the global algorithms to train neural networks.Research Methods/ Statistical Methods,
Combined Global and Local Search for the Falsification of Hybrid Systems
In this paper we solve the problem of finding a trajectory that shows that a
given hybrid dynamical system with deterministic evolution leaves a given set
of states considered to be safe. The algorithm combines local with global
search for achieving both efficiency and global convergence. In local search,
it exploits derivatives for efficient computation. Unlike other methods for
falsification of hybrid systems with deterministic evolution, we do not
restrict our search to trajectories of a certain bounded length but search for
error trajectories of arbitrary length
- …