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# FYP Progress Presentation - PowerPoint PPT Presentation

FYP Progress Presentation. Genetic Algorithm (GA). Presented By:. Oluwaseun Akintimehin. Project Goals. Develop / apply a GA search algorithm to a basic timetable scenario. Visually display results and incorporate low level user interface.

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### FYP Progress Presentation

Genetic Algorithm

(GA)

Presented By:

Oluwaseun Akintimehin

• Develop / apply a GA search algorithm to a basic timetable scenario.

• Visually display results and incorporate low level user interface.

• Define full New Engineering Building (NEB) timetabling model and fitness criteria.

• Test a range of scenarios and verify correctness.

• Define limitations of the system.

• Develop a series of diagrams illustrating each project phase.

• Define a basic timetabling problem.

• Develop a solution to the timetabling problem.

• Apply GA search algorithm to the timetabling solution.

A Basic Timetabling Problem scenario.

A program is asked to read the following input data, and to generate a suitable fitness without taking into account the GA process.

• Number of Modules: 8

• Number of Staffs: 8

• Number of Groups: 2

• Number of Room: 3

• Number of Periods: 12(4 for each room)

• Number of Days: 1

1 scenario.

1

1

2

2

3

2

1

3

1

3

2

4

2

4

2

5

2

5

2

6

3

6

1

7

3

7

1

8

3

8

1

Rules

Module

Group

Staff

Nos. Hours

Limits scenario.

• A staff can not give more than one lecture at the same time period.

• Teaching group can not learn more subjects at the same time period.

• Room can be devoted to only one module in the same time.

• Module can not be assigned time period different from the times stated.

• Group and staff should be assigned to the right module based on the rules.

Solution For Timetabling Problem scenario.

• Initialise variables according to problem definition.

• Mmax = 8 //Max value of Modules

• Gmax = 2 //Max value of Groups

• Smax = 8 //Max value of Staffs

• Rmax = 3 //Max value of Rooms

• Tmax = 4 //Max value of Time periods

• F = Fitness

• for(r=1 to r=Rmax) scenario.

{

for(t=1 to t=tmax)

{

cell[r,t,m] = RandomNumb(1 to Mmax)

cell[r,t,g] = RandomNumb(1 to Gmax)

cell[r,t,s] = RandomNumb(1 to Smax)

}

}

Module is represented as 1 in three dimensional array

Group is represented as 2 in three dimensional array

Staff is represented as 3 in three dimensional array

cell[3,2,3] represents the value of s where r = 3 and t = 2.

1 =1.

1

1

2

2

3

2

1

3

1

3

2

4

2

4

2

5

2

5

2

6

3

6

1

7

3

7

1

8

3

8

1

• Rules

• Modules Groups Staff Nos. Hours

cell(ref)[m,g] allows the checking the group with respect to the module.

cell(ref)[m,s] allows the checking the staff with respect to the module.

nh(ref)[m] allows the checking of the number of hours assigned to the module.

• for(r=1 to r=Rmax)

• {

• for(t=1 to t=Tmax

• {

• F = 2 (default)

• cell[r,t,1] = m

• if(cell[r,t,2] |= cell(ref) [m,g]

• tempFit = 1

• if(cell[r,t,3] |= cell(ref)[m,3]

• tempFit = 1

• else

• tempFit = 0

• }

• F = F + tempFit

• }

• for(r=1 to r=Rmax)

• {

• for(t=1 to t=Tmax)

• {

• Diff = | Nh[r,t,m] – Nh(ref)[m] |

• tempFit = Diff

• Fit = Fit + tempFit

• }

• F = F + Fit

• }

• for(t=1 to t=Tmax)

• {

• for(r=1 to r=Rmax)

• {

• for(s=r+1 to s=Rmax)

• {

• if(cell[r,t,2] = cell[s, t, 2]

• tempFit++

• F=tempFit

• }

• }

• }

• The same code will be used for testing the staff (cell[r,t,3]).

• 1, 2, 2 periods.

5, 2, 3

2, 1, 2

4, 3, 1

6, 3, 2

1,2, 1

3, 3, 5

5, 1, 5

8, 3, 2

1, 1, 1

5, 2, 5

2, 3, 2

7, 1, 4

4, 3, 4

3, 2, 3

4, 2, 4

6, 3, 6

1, 1, 1

3, 1, 3

5, 2, 5

8, 3, 8

7, 3, 7

4, 2, 4

3, 1, 3

1, 1, 1

5, 3, 5

2, 3, 2

4, 2, 4

6, 3, 6

1, 1, 1

3, 1, 3

5, 2, 5

8, 1, 8

7, 2, 7

4, 2, 4

3, 1, 3

• The following are different scenarios of the timetable and their fitness.

• Scenario 1

• Scenario 4

• Fitness = 0

• Scenario 2

Fitness = 24

Fitness = 6

Fitness = 6