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Multi-population Genetic Algorithm Controlled by Information Entropy Based on Floating-point Coding(PDF)


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Multi-population Genetic Algorithm Controlled by Information Entropy Based on Floating-point Coding
LI Chun-lian 1 WANG Xi-cheng 2ZHAO Jin-cheng 3
1. Institute of Computer Science and Technology,Changchun University,Changchun 130022, China;2. State Key Laboratory of Structural Analysis of Industrial Equipment, Dalian University of Technology,Dalian 116023, China;3. Institute of Bio-information and Molecular Design, Dalian University, Dalian 116621,China
g enet ic algorithm quas-i ex act penalty funct ion informational entropy
An improved float ing-point coded genet ic algorithm controlled by informat ion entropy is presented to solve the const rained opt imizat ion problems based on the quas-i exact penalty funct ion. The concept of information entropy is int roduced into the genetic evolut ion by def ining the probability that the opt imal solution located in each populat ion, then a mult-i object ive model including informat ion entropy is constructed. By the use of this model, the probability can be st raightly obtained subsequent ly, the coef ficient of the designed space of v ariables narrow ing dow n for each populat ion can be got to cont rol the populations searching the optimal solution. T he int roduct ion of information ent ropy makes the opt imizat ion procedure more stable and the convergence speed faster. Besides, a new scientific and efficient convergent rule is used in this paper. Numerical examples are given to demonstrate the ef ficiency of the proposed algorithm.


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Last Update: 2013-03-11