IMPACT OF FITNESS SCALING FUNCTIONS ON A BASIC GENETIC  ALGORITHM

Suman Rani, Pushpa Yadav, Sarita Kumari, Mohit Sharma

IMPACT OF FITNESS SCALING FUNCTIONS ON A BASIC GENETIC ALGORITHM

Authors

Suman Rani, Pushpa Yadav, Sarita Kumari, Mohit Sharma

Keywords:

-Genetic algorithm (GA), Fitness function, Fitness scaling, premature convergence, Mutation, Crossover, Selection.

Abstract

One of the heuristic search and optimization strategies still in use today is the genetic algorithm. The task ofminimizing or maximizing the function with several variables while adhering to equality or inequalitylimitations is known as optimization

References

F. Sadjadi, “Comparison of fitness scaling functions in genetic algorithms with applications to optical processing,” in Optical Science and Technology, the SPIE 49th Annual Meeting, 2004, pp. 356–364.

V. Kreinovich, C. Quintana, and O. Fuentes, “Genetic algorithms: what fitness scaling is optimal?,” Cybernetics and Systems, vol. 24, no. 1, pp. 9–26, 1993

Downloads

Published

2024-12-10

How to Cite

Suman Rani, Pushpa Yadav, Sarita Kumari, Mohit Sharma. (2024). IMPACT OF FITNESS SCALING FUNCTIONS ON A BASIC GENETIC ALGORITHM. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 3959–3965. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2720