GAs are adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetics to search for the global optimum of an objective function. An objective function is the target function which is going to be optimized.

An implementation of GA begins with a population of (typically random) chromosomes, where each chromosome is represented by a binary string. One then evaluates these structures and allocates reproductive opportunities in such a way that those chromosomes which represent a better solution to the target problem are given more chances to reproduce"than those chromosomes which are poorer solutions. The goodness"of a solution is typically defined with respect to the current population.

Type the following in MATLAB command window to learn how to write objective function.

help ga

http://in.mathworks.com/help/gads/ga.html

The objective function defined in the following publications may be used for GA as well.

Research Particle Swarm Optimization: Algorithm and its Codes in MATLAB

Code Codes in MATLAB for Particle Swarm Optimization