Open Access
2013 A Mutual-Evaluation Genetic Algorithm for Numerical and Routing Optimization
Chih-Hao Lin, Jiun-De He
J. Appl. Math. 2013(SI15): 1-14 (2013). DOI: 10.1155/2013/214814


Many real-world problems can be formulated as numerical optimization with certain objective functions. However, these objective functions often contain numerous local optima, which could trap an algorithm from moving toward the desired global solution. To improve the search efficiency of traditional genetic algorithms, this paper presents a mutual-evaluation genetic algorithm (MEGA). A novel mutual-evaluation approach is employed so that the merit of selected genes in a chromosome can be determined by comparing the fitness changes before and after interchanging with those in the mating chromosome. According to the determined genome merit, a therapy crossover can generate effective schemata to explore the solution space efficiently. The computational experiments for twelve numerical problems show that the MEGA can find near optimal solutions in all test benchmarks and achieve solutions with higher accuracy than those obtained by eight existing algorithms. This study also uses the MEGA to find optimal flow-allocation strategies for multipath-routing problems. Experiments on quality-of-service routing scenarios show that the MEGA can deal with these constrained routing problems effectively and efficiently. Therefore, the MEGA not only can reduce the effort of function analysis but also can deal with a wide spectrum of real-world problems.


Download Citation

Chih-Hao Lin. Jiun-De He. "A Mutual-Evaluation Genetic Algorithm for Numerical and Routing Optimization." J. Appl. Math. 2013 (SI15) 1 - 14, 2013.


Published: 2013
First available in Project Euclid: 14 March 2014

MathSciNet: MR3096025
Digital Object Identifier: 10.1155/2013/214814

Rights: Copyright © 2013 Hindawi

Vol.2013 • No. SI15 • 2013
Back to Top