Genetic Algorithm is a single objective optimization technique for unconstrained optimization problems.
There are numerous implementations of GA and this one employs SBX Crossover and Polynomial Mutation.
This code is derived from the multi-objective implementation of NSGA-II by Arvind Sheshadari [1].

Note:
(i) Unlike other computational intelligence techniques, the number of functional evaluations cannot be deterministically determined based on the population size and the number of iterations.

(ii) The user defined parameters are (a) the population size, (b) the number of iterations, (c) the distribution index for the SBX operator, (d) the distribution index for polynomial mutation, (e) the tour size in the tournament selction and (f) the crossover probability. In this implementation, the pool size is set to half of the population size (rounded if the population size is an odd number). However this can be changed by the user.

(iii) This implementation ensures monotonic convergence.

References:
(1) https://in.mathworks.com/matlabcentral/fileexchange/10429-nsga-ii--a-multi-objective-optimization-algorithm