Author Archives: Community

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 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. Continue reading

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Introducing IEEE Collabratec

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Cross-Domain Reuse of Extracted Knowledge in Genetic Programming for Image Classification

Genetic programming (GP) is a well-known evolutionary computation technique, which has been successfully used to solve various problems, such as optimization, image analysis, and classification. Transfer learning is a type of machine learning approach … Continue reading

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Surrogate-Assisted Cooperative Swarm Optimization of High-Dimensional Expensive Problems

Surrogate models have shown to be effective in assisting metaheuristic algorithms for solving computationally expensive complex optimization problems. The effectiveness of existing surrogate-assisted metaheuristic algorithms, however, has only been ver… Continue reading

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Matching-Based Selection With Incomplete Lists for Decomposition Multiobjective Optimization

The balance between convergence and diversity is the cornerstone of evolutionary multiobjective optimization (EMO). The recently proposed stable matching-based selection provides a new perspective to handle this balance under the framework of decomposi… Continue reading

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Stochastic Runtime Analysis of the Cross-Entropy Algorithm

This paper analyzes the stochastic runtime of the cross-entropy (CE) algorithm for the well-studied standard problems OneMax and LeadingOnes. We prove that the total number of solutions the algorithm needs to evaluate before reaching the optimal soluti… Continue reading

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Improving Diversity in Evolutionary Algorithms: New Best Solutions for Frequency Assignment

Metaheuristics have yielded very promising results for the frequency assignment problem (FAP). However, the results obtainable using currently published methods are far from ideal in complex, large-scale instances. This paper applies and extends some o… Continue reading

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IEEE Transactions on Evolutionary Computation Society Information

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Seeking Multiple Solutions: An Updated Survey on Niching Methods and Their Applications

Multimodal optimization (MMO) aiming to locate multiple optimal (or near-optimal) solutions in a single simulation run has practical relevance to problem solving across many fields. Population-based meta-heuristics have been shown particularly effectiv… Continue reading

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IEEE World Congress on Computational Intelligence

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