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Nonlinear mixed integer programming problems using genetic algorithm and penalty function

Authors: null Yin-Xiu Li; M. Gen;

Nonlinear mixed integer programming problems using genetic algorithm and penalty function

Abstract

We propose a method for solving nonlinear mixed integer programming (NMIP) problems using genetic algorithms (GAs) and a penalty function method. The penalty function method was used to construct a fitness function to evaluate chromosomes generated from genetic reproduction. Therefore, the mean of satisfactory degrees of systems constraints were introduced. Also, we apply the method for solving optimization problems which belong to nonlinear programming or NMIP problems, using the proposed method. The performance of the proposed method was evaluated through numerical experiments to demonstrate the efficiency of the proposed method.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
12
Average
Top 10%
Average
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