Multiple query evaluation based on an enchanced genetic algorithm

International audienceRecent studies suggest that significant improvement in information retrieval performance can be achieved by combining multiple representations of an information need. The paper presents a genetic approach that combines the results from multiple query evaluations. The genetic algorithm aims to optimise the overall relevance estimate by exploring different directions of the document space. We investigate ways to improve the effectiveness of the genetic exploration by combining appropriate techniques and heuristics known in genetic theory or in the IR field. Indeed, the approach uses a niching technique to solve the relevance multimodality problem, a relevance feedback technique to perform genetic transformations on query formulations and evolution heuristics in order to improve the convergence conditions of the genetic process.The effectiveness of the global approach is demonstrated by comparing the retrieval results obtained by both genetic multiple query evaluation and classical single query evaluation performed on a subset of TREC-4 using the Mercure IRS. Moreover, experimental results show the positive effect of the various techniques integrated to our genetic algorithm model

AQUAGP: Approximate QUery Answering Using Genetic Programming

Speed, cost, and accuracy are crucial performance parameters while evaluating the quality of a query using any Database Management System (DBMS). For some queries it may be possible to approximate the answer using an approximate query answering algorithm or tool. Also, for certain queries, it may not be critical to determine the perfect/exact results so long as the following conditions are true: (a) a high percentage of the relevant data is retrieved correctly, (b) irrelevant or extra data is minimized, and (c) an approximate answer (if available) results in a significant savings in terms of the overall query cost and retrieval time. In this paper we describe a novel approach for approximate query answering using the Genetic Programming (GP) paradigms. We develop an evolutionary computing based query space exploration framework. Given an input query and the database schema, our framework uses tree-based GP to automatically generate and evaluate approximate query candidates. We highlight and discuss different avenues we explored. We evaluate the success of our experiments based on the speed, the cost, and the accuracy of the results retrieved by the re-formulated (GP generated) queries and present the results on a variety of query types for TPC-benchmark and PKDD-benchmark datasets

On the Suitability of Genetic-Based Algorithms for Data Mining

Data mining has as goal to extract knowledge from large databases. A database may be considered as a search space consisting of an enormous number of elements, and a mining algorithm as a search strategy. In general, an exhaustive search of the space is infeasible. Therefore, efficient search strategies are of vital importance. Search strategies on genetic-based algorithms have been applied successfully in a wide range of applications. We focus on the suitability of genetic-based algorithms for data mining. We discuss the design and implementation of a genetic-based algorithm for data mining and illustrate its potentials

BioCloud Search EnGene: Surfing Biological Data on the Cloud

The massive production and spread of biomedical data around the web introduces new challenges related to identify computational approaches for providing quality search and browsing of web resources. This papers presents BioCloud Search EnGene (BSE), a cloud application that facilitates searching and integration of the many layers of biological information offered by public large-scale genomic repositories. Grounding on the concept of dataspace, BSE is built on top of a cloud platform that severely curtails issues associated with scalability and performance. Like popular online gene portals, BSE adopts a gene-centric approach: researchers can find their information of interest by means of a simple “Google-like” query interface that accepts standard gene identification as keywords. We present BSE architecture and functionality and discuss how our strategies contribute to successfully tackle big data problems in querying gene-based web resources. BSE is publically available at: http://biocloud-unica.appspot.com/

Optimizing Photonic Nanostructures via Multi-fidelity Gaussian Processes

We apply numerical methods in combination with finite-difference-time-domain (FDTD) simulations to optimize transmission properties of plasmonic mirror color filters using a multi-objective figure of merit over a five-dimensional parameter space by utilizing novel multi-fidelity Gaussian processes approach. We compare these results with conventional derivative-free global search algorithms, such as (single-fidelity) Gaussian Processes optimization scheme, and Particle Swarm Optimization---a commonly used method in nanophotonics community, which is implemented in Lumerical commercial photonics software. We demonstrate the performance of various numerical optimization approaches on several pre-collected real-world datasets and show that by properly trading off expensive information sources with cheap simulations, one can more effectively optimize the transmission properties with a fixed budget.Comment: NIPS 2018 Workshop on Machine Learning for Molecules and Materials. arXiv admin note: substantial text overlap with arXiv:1811.0075

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