TY - GEN
T1 - Maze generation based on difficulty using genetic algorithm with gene pool
AU - Susanto, Evan Kusuma
AU - Fachruddin, Rifqi
AU - Diputra, Muhammad Ihsan
AU - Herumurti, Darlis
AU - Yunanto, Andhik Ampuh
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9/19
Y1 - 2020/9/19
N2 - Game level design is one of the most important element of developing an enjoyable video game. Besides, game with difficult and dynamic level can make players more exciting. This paper presents a new method of generating a video game level using a genetic algorithm. The proposed method is called gene pool integrates learning. This method implemented in feature selection so that this method is general enough to be used for multiple different types of games. This paper uses some training data to scan good patterns and store all of them in a gene pool. Furthermore, the genetic algorithm is used to find the combination of patterns that can produce the best result. The gene pool also records the quality of each gene so it can learn the pattern which most commonly found in multiple levels. For testing, this research develops a custom game with complicated rules that are hard to represent by a simple 2D array compared to the previously attempted work. The result of this research shows that the method can generate many complicated levels at once. Overall, levels generated using this method on average requires almost 3 times more steps to solve than the dataset.
AB - Game level design is one of the most important element of developing an enjoyable video game. Besides, game with difficult and dynamic level can make players more exciting. This paper presents a new method of generating a video game level using a genetic algorithm. The proposed method is called gene pool integrates learning. This method implemented in feature selection so that this method is general enough to be used for multiple different types of games. This paper uses some training data to scan good patterns and store all of them in a gene pool. Furthermore, the genetic algorithm is used to find the combination of patterns that can produce the best result. The gene pool also records the quality of each gene so it can learn the pattern which most commonly found in multiple levels. For testing, this research develops a custom game with complicated rules that are hard to represent by a simple 2D array compared to the previously attempted work. The result of this research shows that the method can generate many complicated levels at once. Overall, levels generated using this method on average requires almost 3 times more steps to solve than the dataset.
KW - Genetic Algorithm
KW - Machine Learning
KW - Maze Game
KW - Procedural Content Generation
UR - http://www.scopus.com/inward/record.url?scp=85096830950&partnerID=8YFLogxK
U2 - 10.1109/iSemantic50169.2020.9234216
DO - 10.1109/iSemantic50169.2020.9234216
M3 - Conference contribution
AN - SCOPUS:85096830950
T3 - Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020
SP - 554
EP - 559
BT - Proceedings - 2020 International Seminar on Application for Technology of Information and Communication
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Seminar on Application for Technology of Information and Communication, iSemantic 2020
Y2 - 19 September 2020 through 20 September 2020
ER -