Genetic Algorithm for optimization using MATLAB
Year: 2013
Type: article
Abstract: As the applications of systems are increasing in various aspects of our daily life, it enhances the complexity of systems inSoftware design (Program response according to environment) and hardware components (caches, branch predicting pipelines).Withinthe past couple of years the Test Engineers have developed a new testing procedure for testing the correctness of systems: namely theevolutionary test. The test is interpreted as a problem of optimization, and employs evolutionary computation to find the test data withextreme execution times. Evolutionary testing denotes the use of evolutionary algorithms, e.g., Genetic Algorithms (GAs), to supportvarious test automation tasks. Since evolutionary algorithms are heuristics, their performance and output efficiency can vary acrossmultiple runs, there is strong need a environment that can be handle these complexities, Now a day’s MATLAB is widely used for thispurpose. This paper explore potential power of Genetic Algorithm for optimization by using new MATLAB based implementation ofRastrigin’s function, throughout the paper we use this function as optimization problem to explain some key definitions of genetictransformation like selection crossover and mutation. Keywords: Rastrigin’s function, Evolutionary Testing, Genetic Algorithm (GA) , MatLab & Fitness.
Authors: Manish Saraswat, Ajay Sharma
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W2893330629