Optimization of Rosenbrock function usingGenetic Algorithm
Main Article Content
Abstract
Nowadays, Optimization is the most interesting problems to be studied. It is a process of
selecting the best alternative among a given set of options. In the past few decades, a lot of
optimization algorithms came into existence to solve NP Hard problems. Genetic Algorithm is one of
thepopulations based meta-heuristic to solve such problems. Different benchmark functions are
available to test the performance ofoptimization algorithms. Rosenbrock function is a popular test
problem for optimization based algorithms. This paper presents experimental results on optimization
of Rosenbrock function used for performance evaluation of Genetic Algorithm.
Downloads
Metrics
Article Details
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.