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We implemented a Genetic Algorithm (GA) to solve the Traveling Salesman Problem (TSP), aiming to find the shortest route that visits each city once and returns to the starting point. The simulation used 40 and 100 randomly generated cities with an optimized GA approach in MATLAB."

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Introduction

This project is about creating a fuzzy expert system to help form student groups for assignments. Sometimes it’s hard to choose the right groups for assignments. Sometimes it’s hard to choose the right group members because people have different skills, ways of communicating, and levels of responsibility. So, in this project we use fuzzy logic to check those aspects and suggest better group matches in a smarter and more flexible way, just like how a human would decide.

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What are the problems or limitation faced in the project? *Comment here 

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What are the problems or limitation faced in the project? *Comment here 

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NAME: WALTER UNGGANG ANAK NYAMBANG

MATRIC : 86022

ROLE : DOCUMENTATION AND PADLET

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The group assignment fuzzy expert system makes a multi-level type decision based on evaluating students. It is composed of three Fuzzy Inference Systems (FIS) to process different performance aspects made by students and aggregate their intermediate results to create a final Group Match Score. The components and process of the workflow are shown in the figure 1 below.

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Algorithm Design

The GA was designed with the following key elements:
- Encoding
- Fitness Function
- Selection
- Crossover
- Mutation
- Replacement

Algorithm Steps
1. Generate Initial Population (random permutations of cities).
2. Evaluate Fitness for all chromosomes (shorter distance = better).
3. Selection using tournament method.
4. Crossover to produce new offspring.
5. Mutation to introduce variability.
6. Repeat steps 2–5 for a set number of generations (200).
7. Return the best solution found.

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The system is composed of three independent Fuzzy Inference Systems (FIS) that analyze different of a student’s performance and behavior:

1. FIS 1: Skill Level Evaluation

Inputs:

Subject Knowledge (Low, Medium, High)

Technical Ability (Low, Medium, High)

Outputs:

Skill Level (Weak, Moderate, Strong)

2. FIS 2: Communication Styles Evaluation

Inputs:

Responsiveness (Slow, Average, Prompt)

Collaboration Frequency (Rarely, Sometimes, Often)

Outputs:

Team Behavior (Passive, Balanced, Active)

3. FIS 3: Reliability Assessment

Inputs:

Task Completion Rate (Low, Medium, High)

Submission Puntuality (Often Late, Sometimes Late, On Time)

Ouput:

Reliability Level (Unrealiable, Fair, Reliable)

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NAME : CATHERINE IDA ANAK JERY

MATRIC : 86348

ROLE : TEAM LEADER, GENETIC ALGORITHM OPTIMIZATION & DOCUMENTATION

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Each FIS uses a set of fuzzy rules to determine the output based on its inputs. Then, all three outputs are used to compute a Group Match Score (Low, Medium, High), which reflects how well a student may perform in a group setting.

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Sample Fuzzy Rules

Each FIS is driven by IF-THEN rules that reflect expert logic. Example rules include:

a) Skill Level FIS

IF Subject Knowledge is High AND Technical Ability is High THEN Skill Level is Strong.

b) Communication FIS

IF Responsiveness is Prompt AND Collaboration is Often THEN Team Behavior is Active.

c) Reliability FIS

IF Task Completion is Low AND Submission is Often Late THEN Reliability is Unreliable

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Above presents a sample output route for the 40 cities scenario, where cities are represented by blue dots and the optimized travel route is illustrated using red connecting lines.

Case 2: 100 Cities
- Initial Random Distance: ~8200 units
- Best Distance Found: 3154.2 units
- Convergence: ~190 generations

Plot Descriptions:
- City Plot: Blue circles represent cities.
- Optimized Route Plot: Red lines show the shortest route found by GA.

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Name: Nazatul Amira binti Mahri

Matric: 84812

Role: Responsible for system developer

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Coding (1)

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Coding (2)

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Coding (3)

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Coding (5)

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Coding (6)

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Coding (7)

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- Initial Random Distance: ~2700 units
- Best Distance Found: 1235.6 units
- Convergence: ~120 generations

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Name: Nur Ariefah binti Saimi

Matric: 85024

Role: Fuzzy System Research 2 & Documentation

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Name: Kavita Theresa Anak Donald

Matric No: 86202

Role: Fuzzy System Researcher 1 & Documentation

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Name :Tan Hui Mei

No Matric : 87043

Role : System Developer

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Coding (4)

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Figure 3: Rule Editor  and 3D Surface for Skill Level Evaluation FIS

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Rule Base Development and Visualization

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Rule Base Development and Visualization

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The rule editor interface for the `Skill Level Evaluation' Fuzzy Inference System (FIS) is shown in figure 3. On the left-hand side we show the rule base that receives 'knowledge' (Subject Knowledge) and 'Ability' (Technical Ability) as inputs, to compute a student's 'skill_level'. These rules form the logical relationships between input combinations and the particular skill output.

On the right, the 3D surface viewer shows the non-linear correlation between the 'knowledge' and 'Ability' inputs to the output 'skill_level'. This surface serves as a visual indicator of the combined effects of a student's subject knowledge and technical skill on overall ability.

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Figure 4: Rule Editor and 3D Surface for Group Match Score Calculation

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This is one portion of the rule editor of the final FIS responsible for calculating the 'Group Match Score'. 'Knowledge', 'Ability', 'responsiveness', and 'collaboration' (most likely intermediate outputs of other FIS or composite attributes) are used here to infer the final group compatibility score. Rules specify how sets of these attributes result in a particular 'skill_level' (most likely an intermediate output given within this particular set of rules, or maybe there is the global 'skill_level' inputting to this FIS).

The right-hand side 3D surface viewer demonstrates the influence on overall 'GroupMatchScore' of 'Ability' (which could simply be general 'Skill Level' from FIS 1) and 'responsiveness' (from FIS 2). The surface graph is useful for observing how the general system output changes as these significant parameters change, and for obtaining an understanding of how the system determines overall student compatibility for group assignment.

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1. Case 1: Student A

Subject Knowledge: High

Technical Ability: High

Responsiveness: Prompt

Collaboration: Often

Task Completion: Low

Submission: Often Late

→ Outputs: Skill Level: Strong, Team Behavior: Active, Reliability Level: Unreliable, Group Match Score: Medium

Student A possesses good subject expertise and high technical ability, high skill overall. They can respond in a timely manner and work often together, evidencing active team behavior and willingness to work in groups. However, their high frequency of late submission and low percentage of task completion evidence unreliability. Their reliability score is thus low, which lowers their group compatibility overall despite high skills and communication. Overall, Student A’s group match score is medium because their work is inconsistent due to their unreliability in task management.

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Subject Knowledge: Medium

Technical Ability: Medium

Responsiveness: Average

Collaboration: Sometimes

Task Completion: High

Submission: On Time

→ Outputs: Skill Level: Moderate, Team Behavior: Balanced, Reliability Level: Reliable, Group Match Score: High

Student B possesses a medium subject knowledge and technical skill, giving him a medium skill level. His responsiveness is also medium and, at times, collaborative, which displays a balanced team behavior neither dominant nor submissive but assistive when required. With his high task completion and on-time submission, student B is a consistent and reliable performer. These traits make Student B an extremely compatible team player. Hence, Student B’s group match score is high, on the basis of a balanced and consistent team contribution.

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Conclusion

This fuzzy expert system supports the grouping process in education by using intelligent reasoning to evaluate students beyond numerical grades. By analyzing skills, communication behavior, and reliability, it helps in creating balanced teams that are likely to collaborate efficiently. This approach is especially useful for instructors managing large classes or online collaborations.