Interpreting Multi-objective Evolutionary Algorithms via Sokoban Level Generation

June 15, 2024 ยท Declared Dead ยท ๐Ÿ› 2024 IEEE Conference on Games (CoG)

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Authors Qingquan Zhang, Yuchen Li, Yuhang Lin, Handing Wang, Jialin Liu arXiv ID 2406.10663 Category cs.NE: Neural & Evolutionary Cross-listed cs.HC Citations 2 Venue 2024 IEEE Conference on Games (CoG) Last Checked 4 months ago
Abstract
This paper presents an interactive platform to interpret multi-objective evolutionary algorithms. Sokoban level generation is selected as a showcase for its widespread use in procedural content generation. By balancing the emptiness and spatial diversity of Sokoban levels, we illustrate the improved two-archive algorithm, Two_Arch2, a well-known multi-objective evolutionary algorithm. Our web-based platform integrates Two_Arch2 into an interface that visually and interactively demonstrates the evolutionary process in real-time. Designed to bridge theoretical optimisation strategies with practical game generation applications, the interface is also accessible to both researchers and beginners to multi-objective evolutionary algorithms or procedural content generation on a website. Through dynamic visualisations and interactive gameplay demonstrations, this web-based platform also has potential as an educational tool.
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