scholarly journals Interactive genetic algorithm based on typical style for clothing customization

2020 ◽  
Vol 15 ◽  
pp. 155892502092003
Author(s):  
Xinjuan Zhu ◽  
Xuefei Li ◽  
Yifan Chen ◽  
Jingwei Liu ◽  
Xueqing Zhao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Algorithm Design ◽  
Design System ◽  
Initial Population ◽  
Interactive Genetic Algorithm ◽  
Coding Scheme ◽  
Binary Coding ◽  
Speed Up ◽  
Previous Design ◽  
Design Requirements

Companies find it extremely difficult to predict consumers’ needs and requirements, since the spiritual significance of clothing is getting more and more attention. However, most current clothing customization platforms only allow customers to retrieve previous design components from the database and recombine them together, ignoring the customer’s personalized design requirements. In view of the above issues, an intelligent design approach of personalized customized clothing based on typical style and interactive genetic algorithm is proposed in this article. It could generate new fashion styles according to simple customer evolution. The binary coding scheme of suit coat style is presented. And an automatic suit coat design system based on interactive genetic algorithm is developed, in which 10 typical suit coats are selected as the initial population. The experimental results show that the system can alleviate customers’ fatigue and speed up convergence compared with the classic interactive genetic algorithm design, and the designed styles can better meet customers’ preferences.

User’s Preference Aggregation Based on Parallel Interactive Genetic Algorithms

2010 ◽  
Vol 34-35 ◽  
pp. 1159-1164 ◽  
Author(s):  
Yi Nan Guo ◽  
Yong Lin ◽  
Mei Yang ◽  
Shu Guo Zhang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimal Solution ◽  
Small Population ◽  
Population Diversity ◽  
Design System ◽  
Preference Aggregation ◽  
Ip Multicast ◽  
Interactive Genetic Algorithm ◽  
Interactive Genetic Algorithms

In traditional interactive genetic algorithms, high-quality optimal solution is hard to be obtained due to small population size and limited evolutional generations. Aming at above problems, a parallel interactive genetic algorithm based on knowledge migration is proposed. During the evolution, the number of the populations is more than one. Evolution information can be exchanged between every two populations so as to guide themselves evolution. In order to realize the freedom communication, IP multicast is adopted as the transfer protocol to find out the similar users instead of traditional TCP/IP communication mode. Taken the fashion evolutionary design system as test platform, the results indicate that the IP multicast-based parallel interactive genetic algorithm has better population diversity. It also can alleviate user fatigue and speed up the convergence.

Semantic-Based Video Scene Retrieval Using Evolutionary Computing

2011 ◽  
pp. 273-291 ◽  
Author(s):  
Hun-Woo Yoo
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Computing ◽  
Similarity Function ◽  
Initial Population ◽  
Next Generation ◽  
Retrieval Method ◽  
Interactive Genetic Algorithm ◽  
Feature Vectors ◽  
Video Scene

A new emotion-based video scene retrieval method is proposed in this chapter. Five video features extracted from a video are represented in a genetic chromosome and target videos that user has in mind are retrieved by the interactive genetic algorithm through the feedback iteration. After the proposed algorithm selects the videos that contain the corresponding emotion from the initial population of videos, the feature vectors from them are regarded as chromosomes, and a genetic crossover is applied to those feature vectors. Next, new chromosomes after crossover and feature vectors in the database videos are compared based on a similarity function to obtain the most similar videos as solutions of the next generation. By iterating this process, a new population of videos that a user has in mind are retrieved. In order to show the validity of the proposed method, six example categories of “action,” “excitement,” “suspense,” “quietness,” “relaxation,” and “happiness” are used as emotions for experiments. This method of retrieval shows 70% of effectiveness on the average over 300 commercial videos.

scholarly journals 3D modeling design and rapid style recommendation of polo shirt based on interactive genetic algorithm

2020 ◽  
Vol 15 ◽  
pp. 155892502096666
Author(s):  
Zhang Zhuo ◽  
Cong Honglian
Keyword(s):  
Genetic Algorithm ◽  
3D Modeling ◽  
Design Method ◽  
3D Models ◽  
Initial Population ◽  
3D Design ◽  
Interactive Genetic Algorithm ◽  
Component Library ◽  
Suitability Evaluation ◽  
Clothing Design

This paper proposes a design method for personalized 3D modeling and a rapid style recommendation of polo shirts based on interactive genetic algorithms. Through the research on the parametric multi-style design method of polo shirts, this paper proposes 3D design methods such as the conversion of 3D models, component splicing, and stripes matching mapping rules. In addition, we establish a coded component library of style parts and striped designs to realize the component modeling of tailor-made styles for polo shirts. Besides, the Interactive Genetic Algorithm (IGA) is introduced in the article. Through the algorithm coding, initial population generation, selection, using the scoring mechanism to obtain the user’s suitability evaluation of the program, crossover, and mutation, etc., gradually generating a user-satisfied polo shirt style model design plan, a personalized rapid style recommendation of the user-oriented polo shirt is established. This system can present tailored polo shirt style for customers, and WYSIWYG, to find the most favorite clothing styles for customers and lower the threshold of clothing design expertise.

A New Genetic Algorithm Using Order Coding and a Novel Genetic Operator

2013 ◽  
Vol 765-767 ◽  
pp. 662-666
Author(s):  
Yun He Wang ◽  
Li Fang Liu
Keyword(s):  
Genetic Algorithm ◽  
Experimental Results ◽  
Algorithm Analysis ◽  
Initial Population ◽  
Mutation Operator ◽  
Optimal Solutions ◽  
Mutation Process ◽  
Coding Method ◽  
Speed Up ◽  
And Migration

This paper focuses on improving the efficiency of the genetic algorithm, analysis the shortages of the algorithm at coding method and mutation operator and selected a TSP problem to simulate. First of all, the initial population was taken by order coding to speed up the algorithm convergence. In addition, the inversion operation and migration operation had been added into the mutation process. The experimental results show that without sacrificing convergence speed and the scale of population the improved algorithm has an extraordinary increase on optimal solutions and efficiency.

scholarly journals Computer-assisted Music Composition Algorithm Design Dependent on Interactive Genetic Algorithm with Interval Fitness

2021 ◽  
Vol 2066 (1) ◽  
pp. 012035
Author(s):  
Qili Guo
Keyword(s):  
Genetic Algorithm ◽  
Music Composition ◽  
Algorithm Design ◽  
New Music ◽  
Computer Assisted ◽  
Small Data ◽  
Data Set ◽  
Interactive Genetic Algorithm ◽  
Small Data Set ◽  
Simulation Results

Abstract Computer-assisted music composition refers to computer-assisted music composition with the participation of people. However, there are problems such as style and expression. In this paper, a computer-assisted music composition algorithm based on the interactive genetic algorithm with interval fitness is proposed. A new music prediction model is established by integrating melody units and rhythms into traditional models with only notes or rhythms as units. Moreover, the generated music phrases are optimized by the interactive genetic algorithmphrase. The simulation results suggest that the proposed algorithm can generate music phrases quickly with a certain melody logic that conforms to the personal demand of users using a small data set.

Network Coding Optimization Method Research Based on Genetic Algorithm

2014 ◽  
Vol 644-650 ◽  
pp. 2059-2062
Author(s):  
Hong Yan Yan
Keyword(s):  
Genetic Algorithm ◽  
Network Coding ◽  
Optimization Method ◽  
Premature Convergence ◽  
Initial Population ◽  
Running Time ◽  
Coding Scheme ◽  
Algorithm Search

Network coding optimization method research based on genetic algorithm applies network coding technology in monophyletic multicast network. After reaching network multicast rate, find link coding scheme which makes the minimum of the total number of network coding. Moreover, it makes the analysis and improvement for general genetic algorithm’s defects in network coding link optimization such as rare individual successful decoded by randomly generated initial population strategies, reduced algorithm search ability, premature convergence of genetic algorithm and long algorithm running time.

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