Genetic Programming and Evolvable Machines
, Volume 14, Issue 4, pp 395427
A high performance genetic algorithm using bacterial conjugation operator (HPGA)
 Amir MehrafsaAffiliated withSchool of Engineering Emerging Technologies, University of Tabriz
 , Alireza SokhandanAffiliated withSchool of Engineering Emerging Technologies, University of Tabriz
 , Ghader KarimianAffiliated withFaculty of Electrical and Computer Engineering, University of Tabriz Email author
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In this paper an efficient evolutionary algorithm is proposed which could be applied to realtime problems such as robotics applications. The only parameter of the proposed algorithm is the “Population Size” which makes the proposed algorithm similar to parameterless algorithms, and the only operator applied during the algorithm execution is the bacterial conjugation operator, which makes using and implementation of the proposed algorithm much easier. The procedure of the bacterial conjugation operator used in this algorithm is different from operators of the same name previously used in other evolutionary algorithms such as the pseudo bacterial genetic algorithm or the microbial genetic algorithm. For a collection of 23 benchmark functions and some other wellknown optimization problems, the experimental results show that the proposed algorithm has better performance when compared to particle swarm optimization and a simple genetic algorithm.
Keywords
Evolutionary algorithm Genetic algorithm Bacterial conjugation High performance Realtime Parameter less Title
 A high performance genetic algorithm using bacterial conjugation operator (HPGA)
 Journal

Genetic Programming and Evolvable Machines
Volume 14, Issue 4 , pp 395427
 Cover Date
 201312
 DOI
 10.1007/s107100139185x
 Print ISSN
 13892576
 Online ISSN
 15737632
 Publisher
 Springer US
 Additional Links
 Topics
 Keywords

 Evolutionary algorithm
 Genetic algorithm
 Bacterial conjugation
 High performance
 Realtime
 Parameter less
 Authors

 Amir Mehrafsa ^{(1)}
 Alireza Sokhandan ^{(1)}
 Ghader Karimian ^{(2)}
 Author Affiliations

 1. School of Engineering Emerging Technologies, University of Tabriz, Tabriz, Iran
 2. Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran