Personalized Search Inspired Fast Interactive Estimation of Distribution Algorithm and Its Application

Interactive evolutionary algorithms have been applied to personalized search, in which less user fatigue and efficient search are pursued. Motivated by this, we present a fast interactive estimation of distribution algorithm (IEDA) by using the domain knowledge of personalized search. We first induc...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation 2017-08, Vol.21 (4), p.588-600
Main Authors: Yang Chen, Xiaoyan Sun, Dunwei Gong, Yong Zhang, Jong Choi, Klasky, Scott
Format: Article
Language:English
Subjects:
Algorithms
Bayes methods
Bayesian analysis
Domain knowledge
Estimation
Evolutionary algorithms
Fatigue
Fitness
IEC
interactive estimation of distribution algorithm (IEDA)
naive Bayesian model
Neural networks
Optimization
personalized search
Probabilistic logic
Probabilistic methods
Probability theory
Radial basis function
Searching
Social factors
Sociology
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Summary:Interactive evolutionary algorithms have been applied to personalized search, in which less user fatigue and efficient search are pursued. Motivated by this, we present a fast interactive estimation of distribution algorithm (IEDA) by using the domain knowledge of personalized search. We first induce a Bayesian model to describe the distribution of the new user's preference on the variables from the social knowledge of personalized search. Then we employ the model to enhance the performance of IEDA in two aspects, that is: 1) dramatically reducing the initial huge space to a preferred subspace and 2) generating the individuals of estimation of distribution algorithm(EDA) by using it as a probabilistic model. The Bayesian model is updated along with the implementation of the EDA. To effectively evaluate individuals, we further present a method to quantitatively express the preference of the user based on the human-computer interactions and train a radial basis function neural network as the fitness surrogate. The proposed algorithm is applied to a laptop search, and its superiorities in alleviating user fatigue and speeding up the search procedure are empirically demonstrated.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2017.2657787