ELE8066 - Intelligent Systems and Control - Engineering Assignment Help

Assignment Task

The basic problem you are required to solve using GAs is to find the maximum of the function F(x,y) in  the range x=[?r, r], y=[?r, r],  where F(x,y) is given by the Matlab function myOptFunc.m, i.e. F(x,y)  =  myOptFunc(x,y,SN)  where SN is your student number. and the value r is equal to 6 + the final digit of your student number. For example, if your student  number is 40879135 then r = 11.  Plots of typical surfaces corresponding to this function are shown in

In addition to the myOptFunc.m file, you have been provided with a set of functions and a script that  implement  a  basic  binary  GA  to  find  the  maximum  of  this  function.  The  script  file  is  called  Run_GA_basic.m.   Preliminaries: Open this script in the Matlab editor and change the value of the SN variable at the top  of the file to your student number, and the value of the range variable to  6 + the final digit of your  student number. Then save and run  the  file and observe the plots that are generated. Run  the  file  again and this time set visflag=0 (also defined at the top of the script).  Using visflag=1 allows you to see  graphically how the population changes from one generation to the next in the GA, but is slow to run.  Switch between these settings as needed for the different parts of the assignment. 

4. Modify the ‘Run_GA_basic’ script so that the GA incorporates elitist selection, where the k fittest  parents  are  copied  directly  into  the  next  generation  together  with  the  pop_size  ?  k  fittest  offspring. Explore the performance of elitist GA for different values of k, gen_max and pop_size when  the mutation  rate is 0.1. Hence deduce appropriate values of  these parameters  for  this  problem.  How  does  the  performance  of  the  elitist  GA  compare  with  the  basic  GA  implementation?

5. Rather than using binary genes and chromosomes, design and implement a GA that uses real? valued  genes  to  solve  the  problem.  This  means  that  chromosomes,  crossover  and  mutation  operators  work  directly  with  real  valued  genes,  and  the  associated  functions  will  need  to  be  changed accordingly.

6. Compare  and  contrast  the  performance  of  binary  versus  real  valued  GAs  for  solving  the  optimisation problem

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