Journal of Optimization Theory and Applications

, Volume 156, Issue 2, pp 450–468

Hidden Genes Genetic Optimization for Variable-Size Design Space Problems


DOI: 10.1007/s10957-012-0122-6

Cite this article as:
Abdelkhalik, O. J Optim Theory Appl (2013) 156: 450. doi:10.1007/s10957-012-0122-6


This paper introduces the biologically inspired concept of hidden genes genetic algorithms; they search for optimal solutions to global optimization problems of multimodal objective functions with a variable number of design variables. A fixed chromosome length is assumed for all solutions in the population. Each chromosome is divided into effective and ineffective segments. The effective segment includes the design variables for that solution. The ineffective segment includes only hidden genes. Hidden genes are excluded in objective function evaluations. The effect of the hidden genes on the convergence of the genetic algorithm is studied. Two test cases are presented.


Global optimizationVariable length genetic algorithmsHidden genes genetic algorithms

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Mechanical Engineering-Engineering Mechanics DepartmentMichigan Tech UniversityHoughtonUSA