چکیده

A genetic algorithm has been used to search for artificial intelligence and computing discipline. It assists researchers in finding the most optimized solutions to search problems based on the theory of natural selection and evolutionary biology. Genetic algorithms are robust search algorithm which is appropriate for search in large and complex data sets. There are many ways to produce the individuals in GA through using the crossover and mutation techniques. The final vision of any GA is to maximize fitness function. This paper has proposed a new technique for genetic algorithm uniform crossover by optimizing previous methods. Proposed techniques are developed and tested in the MATLAB platform to see and evaluate the gained result with current crossover techniques. The result shows meaningful improvement in terms of reducing the number of iterations and function evaluations. 

کلیدواژه:

Genetic algorithm

uniform crossover

discrete problems

iteration

function evaluation

نویسندگان :

S.Slehinasab¹, Javad Hosseinkhani², F.Jaryani³, H. Rahmani⁴, S.N Moafinejad⁵, Mehdi Gheisari⁶

¹School of Engineering, Lorestan University Aleshtar Campus, Aleshtar, Iran.

²Department of Computer Engineering, Damavand Branch, Islamic Azad University, Damavand, Iran

³Faculty of Computer Science, University of, Technology Malaysia, Johor, Malaysia.

⁴Arkan Felez Co., Qazvin, Iran.
⁵Physics department , Shahid Beheshti University, Tehran, Iran.

⁶Department of Computer Engineering, Damavand Branch, Islamic Azad University, Damavand, Iran.

DOR:

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