scholarly journals RankGPES: learning to rank for information retrieval using a hybrid genetic programming with evolutionary strategies

2021 ◽  
Author(s):  
Mohammad A. Islam
Keyword(s):  
Information Retrieval ◽  
Evolutionary Algorithms ◽  
Genetic Programming ◽  
Large Volume ◽  
Learning To Rank ◽  
Hybrid Approach ◽  
Industrial Applications ◽  
Evolutionary Strategies ◽  
Optimal Result ◽  
Approach To Learning

In recent years, Learning to Rank has not only shown effectiveness and better suitability for modern Web Era needs, but also has proved that it outperforms traditional ranking in terms of accuracy and efficiency. Evolutionary approach to Learning to Rank such as RankGP [37] and RankDE [3] have shown further improvement over non-evolutionary algorithms. However when Evolutionary algorithms have been applied to a large volume of data, often they showed they required so much computational efforts that they were not worth applying to industrial applications. In this thesis, we present RankGPES: a Learning to Rank algorithm based on a hybrid approach combining Genetic Programming with Evolution Strategies. Our results not only showed that it outperformed both RankGP [37] by 20% and RankDE [3] by 6% in terms of accuracy but also it showed it required significant less amount of time to converge to a near-optimal result.

scholarly journals RankGPES: learning to rank for information retrieval using a hybrid genetic programming with evolutionary strategies

2021 ◽  
Author(s):  
Mohammad A. Islam
Keyword(s):  
Information Retrieval ◽  
Evolutionary Algorithms ◽  
Genetic Programming ◽  
Large Volume ◽  
Learning To Rank ◽  
Hybrid Approach ◽  
Industrial Applications ◽  
Evolutionary Strategies ◽  
Optimal Result ◽  
Approach To Learning

In recent years, Learning to Rank has not only shown effectiveness and better suitability for modern Web Era needs, but also has proved that it outperforms traditional ranking in terms of accuracy and efficiency. Evolutionary approach to Learning to Rank such as RankGP [37] and RankDE [3] have shown further improvement over non-evolutionary algorithms. However when Evolutionary algorithms have been applied to a large volume of data, often they showed they required so much computational efforts that they were not worth applying to industrial applications. In this thesis, we present RankGPES: a Learning to Rank algorithm based on a hybrid approach combining Genetic Programming with Evolution Strategies. Our results not only showed that it outperformed both RankGP [37] by 20% and RankDE [3] by 6% in terms of accuracy but also it showed it required significant less amount of time to converge to a near-optimal result.

On Measuring the Attributes of Evolutionary Algorithms

2011 ◽  
pp. 332-348
Author(s):  
J. L. Fernandez-Villacanas Martin ◽  
P. Marrow ◽  
M. Shackleton
Keyword(s):  
Genetic Algorithm ◽  
Information Retrieval ◽  
Evolutionary Algorithms ◽  
Genetic Programming ◽  
Selection Process ◽  
Relative Effectiveness ◽  
Evolutionary Process ◽  
Neutral Evolution ◽  
Adaptive Landscape ◽  
The Future

In this chapter we compare the performance of two contrasting evolutionary algorithms addressing a similar problem, of information retrieval. The first, BTGP, is based upon genetic programming, while the second, MGA, is a genetic algorithm. We analyze the performance of these evolutionary algorithms through aspects of the evolutionary process they undergo while filtering information. We measure aspects of the variation existing in the population undergoing evolution, as well as properties of the selection process. We also measure properties of the adaptive landscape in each algorithm, and quantify the importance of neutral evolution for each algorithm. We choose measures of these properties because they appear generally important in evolution. Our results indicate why each algorithm is effective at information retrieval, however they do not provide a means of quantifying the relative effectiveness of each algorithm. We attribute this difficulty to the lack of appropriate measures available to measure properties of evolutionary algorithms, and suggest some criteria for useful evolutionary measures to be developed in the future.

On Measuring the Attributes of Evolutionary Algorithms

2011 ◽  
pp. 281-302
Author(s):  
J.-L. Fernandez-Villacanas Martin ◽  
P. Marrow ◽  
M. Shackleton
Keyword(s):  
Genetic Algorithm ◽  
Information Retrieval ◽  
Evolutionary Algorithms ◽  
Genetic Programming ◽  
Selection Process ◽  
Relative Effectiveness ◽  
Evolutionary Process ◽  
Neutral Evolution ◽  
Adaptive Landscape ◽  
The Future

In this chapter we compare the performance of two contrasting evolutionary algorithms addressing a similar problem, of information retrieval. The first, BTGP, is based upon genetic programming, while the second, MGA, is a genetic algorithm. We analyze the performance of these evolutionary algorithms through aspects of the evolutionary process they undergo while filtering information. We measure aspects of the variation existing in the population undergoing evolution, as well as properties of the selection process. We also measure properties of the adaptive landscape in each algorithm, and quantify the importance of neutral evolution for each algorithm. We choose measures of these properties because they appear generally important in evolution. Our results indicate why each algorithm is effective at information retrieval, however they do not provide a means of quantifying the relative effectiveness of each algorithm. We attribute this difficulty to the lack of appropriate measures available to measure properties of evolutionary algorithms, and suggest some criteria for useful evolutionary measures to be developed in the future.

scholarly journals Development of Multiple Behaviors in Evolving Robots

Robotics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 1
Author(s):  
Victor Massagué Respall ◽  
Stefano Nolfi
Keyword(s):  
Evolutionary Algorithms ◽  
State Of The Art ◽  
Evolutionary Robotics ◽  
The Other ◽  
Evolutionary Strategies ◽  
Multiple Behaviors ◽  
Pareto Fronts ◽  
Expected Fitness

We investigate whether standard evolutionary robotics methods can be extended to support the evolution of multiple behaviors by forcing the retention of variations that are adaptive with respect to all required behaviors. This is realized by selecting the individuals located in the first Pareto fronts of the multidimensional fitness space in the case of a standard evolutionary algorithms and by computing and using multiple gradients of the expected fitness in the case of a modern evolutionary strategies that move the population in the direction of the gradient of the fitness. The results collected on two extended versions of state-of-the-art benchmarking problems indicate that the latter method permits to evolve robots capable of producing the required multiple behaviors in the majority of the replications and produces significantly better results than all the other methods considered.

Open System for Indexing and Retrieving Multimedia Information

2013 ◽  
Author(s):  
Hanene Maghrebi ◽  
Amos David
Keyword(s):  
Information Retrieval ◽  
Multimedia Information ◽  
Open System ◽  
Large Volume ◽  
Retrieval System ◽  
Main Idea ◽  
Relevant Information ◽  
Information Retrieval System ◽  
Multimedia Content ◽  
Nous Proposons

Managing the increasing growth of multimedia content still poses some problems. The challenge is to propose relevant information to the users among the large volume of information available. The main idea that drives our approach is to provide an open information retrieval system, which can adapt its results to several…La gestion de l’information multimédia soulève encore quelques problèmes. Le défi est de pouvoir proposer à l’utilisateur des informations pertinentes parmi la quantité d’information qui ne cesse de s’accroître. Dans cette lignée, nous proposons un système ouvert de recherche d’information capable d’adapter ses résultats aux différents… 

Improve Biomedical Information Retrieval Using Modified Learning to Rank Methods

2018 ◽  
Vol 15 (6) ◽  
pp. 1797-1809 ◽  
Author(s):  
Bo Xu ◽  
Hongfei Lin ◽  
Yuan Lin ◽  
Yunlong Ma ◽  
Liang Yang ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Learning To Rank ◽  
Biomedical Information Retrieval
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