NDLI: Degree preserving based crossover for constrained optimization problems

Content Provider	IEEE Xplore Digital Library
Author	Chenggang Cui Tiejun Wu
Copyright Year	2010
Description	Author affiliation: Department of Control Science & Engineering, Zhejiang University, Hangzhou, China (Chenggang Cui; Tiejun Wu)
Abstract	Real-coded genetic algorithms (RCGAs) have been effectively used to solve constrained optimization problems (COPs). However, the crossover operators do not have mechanisms to handle constraints and there is no guarantee that if the parents satisfy some constraints the offspring will satisfy them as well. Degree preserving based crossover operators are proposed to increase the probability of constructing feasible offspring and preserve the generated partial solutions which satisfy some constraints. A new concept of the feasibility degree (FD) of the building blocks of feasible solutions (BBFSs) based on the schema theorem and building block hypothesis is also proposed to measure the probability of an individual which contains the BBFS being a feasible solution. Therefore, feasible offspring can be constructed by mixing the building blocks of feasible solutions and the generated partial solutions which satisfy some constraints can be preserved. The theoretical validity of the new crossover operators is proved by the schema theorem and the mixing ladder climbing model. Finally, the proposed crossover operators were tested with benchmark problems and the results proved the efficiency of the operators.
Starting Page	3094
Ending Page	3099
File Size	620492
Page Count	6
File Format	PDF
ISBN	9781424467129
e-ISBN	9781424467129
DOI	10.1109/WCICA.2010.5553826
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-07
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Optimization Probability Bismuth Linear matrix inequalities Biological cells Acceleration Analytical models Constrained optimization Genetic algorithm Crossover Degree preserving
Content Type	Text
Resource Type	Article

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