A New Position Based Fitness Evaluation for Genetic Algorithm
P.Vijayalakshmi, M.Ayyappan, K. Annaram
International Journal of Web Applications, Vol. 3, No. 3, 2011.
DOI: 
Link: http://www.dline.info/ijwa/fulltext/v3n3/1.pdf
Abstract:
In this paper, a new approach for fitness evaluation of Genetic Algorithm (GA) is discussed. The approach is based on position of the genes in a chromosome. A random initial population sometimes may generate recessive strings that may increase the number of iterations. Also, it is hard to decide time to arrive at a particular solution. Chromosome’s strength is dependent on the arrangement of its genes. A new position based fitness evaluation technique is introduced, where fitness is assigned to each gene’s position to compute the strength of a chromosome. Also selecting the weakest position for crossing over avoids the probability of dummy iterations and increases the efficiency. We have tested the new approach on “N” Queen Problem that represents NP hard problems. The experimental results are comparatively good with other state of art GAs, simple genetic algorithm (SGA), enhanced improved genetic algorithm (EIGA) and adaptive genetic algorithm (AGA). The strength of new fitness approach increases in fitness in each iteration and efficient. Potential application includes search techniques and machine learning.