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Information 2019, 10(2), 36; https://doi.org/10.3390/info10020036

Interactive Genetic Algorithm Oriented toward the Novel Design of Traditional Patterns

Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, China
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Received: 23 November 2018 / Revised: 25 December 2018 / Accepted: 8 January 2019 / Published: 22 January 2019
(This article belongs to the Section Information Applications)
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Abstract

To create alternative complex patterns, a novel design method is introduced in this study based on the error back propagation (BP) neural network user cognitive surrogate model of an interactive genetic algorithm with individual fuzzy interval fitness (IGA-BPFIF). First, the quantitative rules of aesthetic evaluation and the user’s hesitation are used to construct the Gaussian blur tool to form the individual’s fuzzy interval fitness. Then, the user’s cognitive surrogate model based on the BP neural network is constructed, and a new fitness estimation strategy is presented. By measuring the mean squared error, the surrogate model is well managed during the evolution of the population. According to the users’ demands and preferences, the features are extracted for the interactive evolutionary computation. The experiments show that IGA-BPFIF can effectively design innovative patterns matching users’ preferences and can contribute to the heritage of traditional national patterns. View Full-Text
Keywords: patterns with traditional national characteristics; BP neural network; surrogate model; interactive genetic algorithm; aesthetic evaluation patterns with traditional national characteristics; BP neural network; surrogate model; interactive genetic algorithm; aesthetic evaluation
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Lv, J.; Zhu, M.; Pan, W.; Liu, X. Interactive Genetic Algorithm Oriented toward the Novel Design of Traditional Patterns. Information 2019, 10, 36.

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