This project aims to apply a Genetic Algorithm to solve the Mastermind Game. The Mastermind Game in our study has 2 game modes: P(6,4) and P(8,5) in the limit of guesses of 10 and 12 turns, respectively.
- To decrease the average round of guesses as much as possible
- To decrease execution time usage
- TEST RUN: 1000 games
- Min Round: 1
- Max Round: 7
- Average Round: 4.542
- Average time: 10.243 ms per game
- Winrate: 100%
- TEST RUN: 1000 games
- Min Round: 2
- Max Round: 9
- Average Round: 5.811
- Average time: 560.853 ms per game
- Winrate: 100%
- Tournament Selection
- Single-Point Crossover
- Two-Point Crossover
- Random Resetting
- Inversion
- Swap (Permutation)
#define TOURNAMENT_SIZE 2
#define CROSSOVER_RATE 10
#define MUTATION_RATE 10
#define PERMUTATION_RATE 2
#define INVERSION_RATE 3
#define EHAT_POPULATION_SIZE 20
#define POPULATION_SIZE 30
#define MAX_GENERATIONS 20
#define ELITSM_POPULATION_RATE 0.3
#define FITNESS_THRESHOLD 0.0
#define FITNESS_BLACK_VALUE 1.0
#define FITNESS_WHITE_VALUE 1.0
- CROSSOVER_RATE is the ratio between single-point and two-point crossover. Therefore MUTATION_RATE 10 means a 10% chance of using a single-point crossover and a 90% chance of using a two-point crossover (10/90)
- PERMUTATION_RATE is the chance of being mutated by the "swap mutation" method
- ELITSM_POPULATION_RATE is the rate of elitism from the eligible set
- The eligible set noted by "EHAT"
- The fitness value converges to 0 is better
Berghman et. al. (2009) Efficient solutions for Mastermind using genetic algorithms
- This project is a part of the CPE231 Algorithm subject in Semester 1/2023
- Department of Computer Engineering, King Mongkut's University of Technology Thonburi
Members
| Student ID | Name | Github |
|---|---|---|
| 65070501039 | Pongpon Butseemart | Pongpon00 |
| 65070501055 | Sorrawit Udomwittayakrai | SorrawitUdom |
| 65070501069 | Kanitsorn Darunaitorn | Dr804t |
| 65070501083 | Panurut Sirinapaisan | sokungz01 |
| 65070501092 | Park Saranunt | Saranunt |