Real-coded genetic algorithm with variable rates of cross-over and mutation: a basis of global optimization for multi-modal functions via interval technique
DOI10.1080/00207160601140174zbMath1115.65066OpenAlexW1999709953MaRDI QIDQ3438782
Syeda Darakhshan Jabeen, Asoke Kumar Bhunia
Publication date: 29 May 2007
Published in: International Journal of Computer Mathematics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00207160601140174
global optimizationnumerical examplesnonconvex programminggenetic algorithminterval techniquemulti-modal continuous function
Numerical mathematical programming methods (65K05) Nonconvex programming, global optimization (90C26) Stochastic programming (90C15) Interval and finite arithmetic (65G30)
Cites Work
- Unnamed Item
- Unnamed Item
- A hybrid genetic algorithm for nonconvex function minimization.
- Differential evolution -- a simple and efficient heuristic for global optimization over continuous spaces
- A hybrid algorithm for identifying global and local minima when optimizing functions with many minima.
- A hybrid descent method for global optimization
- Population set-based global optimization algorithms: some modifications and numerical studies
- Hybrid genetic algorithm for optimization problems with permutation property
- Optimisation With Real-Coded Genetic Algorithms Based On Mathematical Morphology
- A continuous genetic algorithm designed for the global optimization of multimodal functions
- Simultaneously applying multiple mutation operators in genetic algorithms
- Genetic algorithms and fuzzy multiobjective optimization
This page was built for publication: Real-coded genetic algorithm with variable rates of cross-over and mutation: a basis of global optimization for multi-modal functions via interval technique
