Izvorni znanstveni članak
https://doi.org/10.17535/crorr.2022.0008
A new fitness-based selection operator for genetic algorithms to maintain the equilibrium of selection pressure and population
Fakhra Batool Naqvi
orcid.org/0000-0002-0086-3477
; Department of Statistics, Quaid-i-Azam University
Muhammad Yousaf Shad
; Department of Statistics
Sažetak
A genetic algorithm is one of the best optimization techniques for solving complex nature optimization problems. Different selection schemes have been proposed in the literature to address the
major weaknesses of GA i.e., premature convergence and low computational efficiency. This article proposed a new selection operator that provides a better trade-off between selection pressure and
population diversity while considering the relative importance of each individual. The average accuracy of the proposed operator has been measured by χ2 goodness of fit test. It has been performed on two different populations to show its consistency. Also, its performance has been evaluated on fourteen benchmark problems while comparing it with competing selection operators. Results show the effective
performance in terms of two statistics i.e., less average and standard deviation values. Further, the performance indexes and the GA convergence show that the proposed operator takes better care of
selection pressure and population diversity.
Ključne riječi
genetic algorithm; optimization; premature convergence; population diversity; selection operators
Hrčak ID:
280267
URI
Datum izdavanja:
12.7.2022.
Posjeta: 803 *