Feasible–Infeasible Two-Population Genetic Algorithm to evolve dungeon levels with dependencies in barrier mechanics

Abstract

This paper presents a search-based solution for the generation of dungeon levels with barrier mechanics and the placement of challenges and rewards in the levels’ rooms. The barrier is a feature that temporarily blocks the player’s progression, where one or more keys will unblock the way. The placement of barriers and keys must satisfy some constraints since the player cannot be stuck during the gameplay. Feasible–Infeasible Two-Population Genetic Algorithm (FI2Pop GA) evolves a grid representation that handles the level dependencies of barrier mechanics. We propose the concept of ordered regions to control the availability of keys better in the levels and procedures to create levels with more diversity in their contents. Data to measure the variety of the generated content is collected based on map linearity, mission linearity, leniency, and path redundancy. We analyzed our results through expressive range analysis, and it shows that our approach can generate a wide variety of playable levels.