I have attached the Matlab code [url removed, login to view] program should be solving two tasks (two sudoku puzzles of high/low similarities) by default . You should send two tasks from callsolver.m file by default (not one) . I can send you some paper on MFEA algorithm if needed for better understanding . Programming should be done in matlab . I just want you to edit my code , not write a new one .
It is important that you have experience conducting research and an expert in matlab and genetic algorithms because I am looking to learn and improve from you . If your initial work is satisfactory , more milestones will be added and I won't be hesitant to increase budget if you can be relied on for 2 years .
Milestone 1 (10 GBP) : read the PDF paper and code attached and explain the program .Put comments on each line of matlab code . Also , In a word document , give a step by step explanation on how the Multi Factorial Evolution Algorithm solves two sudoku tasks at once , explaining each variable , each file , function and it's duties . Explain How the MFEA groups tasks and how each loop works . How the variables scalar rank and scalar fitness work in optimising and finding similarities between tasks . Also why certain commands like parfor are preferred in particular coding lines .
Milestone 2 (15 GBP ) :modify the program to make it faster and efficient . Do not change the original Multi factorial technique which uses scalar fitness and scalar rank variables . Modify existing components of the algorithm like mutation and crossover techniques and values etc . Maybe by grouping tasks more efficiently .
Milestone 3 (35 GBP): make a gui for the program . When the user runs the program there should be visuals of two sudoku boxes which pop up . The user can input numbers he wants in some of the sudoku boxes and then enter mutation and other parameter values . Then the program starts when the user presses execute . In realtime while matlab searches for best solutions , each population should be filled in both sudoku boxes and keep changing as the program converges to the best population .
The gui should also show population entropy , generation , best solution data changing on the side while program runs. At the end of program display necessary graphs .
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