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Remarks on Invariant Method of the Second-Order Linear Differential Equations with Variable Coefficients Read chapter Read first chapter

2013 | OriginalPaper | Chapter

Multi-Objective Genetic Algorithm with Complex Constraints Based on Colony Classify

Authors : Li-li Zhang, Feng Xu, Juan Hu

Published in: Proceedings of The Eighth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), 2013

Publisher: Springer Berlin Heidelberg

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Abstract

The paper presents a constraint-handling approach for multi-objective optimization. The general idea is shown as follow: Firstly, the population was classified into two groups: feasible population and infeasible population. Secondly, feasible population was classified into Pareto population and un-Pareto population. Thirdly, the Pareto population was defied with k-average classify approach into colony Pareto population and in-colony Pareto population. Last, R-fitness was given to each population. Simulation results show that the algorithm not only improves the rate of convergence but also can find feasible Pareto solutions distribute abroad and even.

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previous chapter An Iterative Algorithm for the Generalized Center Symmetric Solutions of a Class of Linear Matrix Equation and Its Optimal Approximation
next chapter Hypothesis Testing for Squared Radial Ornstein–Uhleneck Model: Moderate Deviations Method
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Metadata
Title
Multi-Objective Genetic Algorithm with Complex Constraints Based on Colony Classify
Authors
Li-li Zhang
Feng Xu
Juan Hu
Copyright Year
2013
Publisher
Springer Berlin Heidelberg
Book
Proceedings of The Eighth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), 2013

Print ISBN: 978-3-642-37501-9

Electronic ISBN: 978-3-642-37502-6

Copyright Year: 2013

DOI
https://doi.org/10.1007/978-3-642-37502-6_20

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