On the performance of MATLAB's inbuilt genetic algorithm on single and multi-objective unconstrained optimization problems

Genetic Algorithm (GA) is a very popular evolutionary technique that has been used for single and multi-objective optimization problems. MATLAB, a widely used technical computing language, has its own variant of these algorithms included along with its optimization toolbox. Since this allows for easy implementation, it has been widely used to solve various engineering problems. The performances of the inbuilt single and multi-objective algorithms of GA at default settings are studied on a total of 29 benchmark functions. The single objective ga is tested on a set of fourteen popularly used single objective benchmark functions whereas the multi-objective gamultiobj is tested on five of the Zitzler-Deb-Thiele's (ZDT) problems and the ten unconstrained problems from the CEC2009 algorithm competition.