Abstract
This paper presents an evolutionary approach to procedural content generation of 2D maps for computer games. To provide better adaptability to the map designer’s vision, user preference is incorporated to guide the algorithm. A cooperative method utilizes novelty search as a source of diverse solutions, which are then further optimized by multiple, subsequent genetic algorithms. We compare the results to a second approach based on multi-objective optimization, which takes the two conflicting goals of optimizing towards user preference and finding novel solutions as objective functions to build a Pareto front of maps.
Original language | English |
---|---|
Title of host publication | GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference |
Editors | Tobias Friedrich |
Publisher | ACM Sigevo |
Pages | 39-40 |
Number of pages | 2 |
ISBN (Electronic) | 9781450343237 |
ISBN (Print) | 978-1-4503-4323-7 |
DOIs | |
Publication status | Published - 20 Jul 2016 |
Event | Genetic and Evolutionary Computation Conference (GECCO 2016) - Denver, Colorado, United States Duration: 20 Jul 2016 → 24 Jul 2016 http://gecco-2016.sigevo.org/ |
Publication series
Name | GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference |
---|
Conference
Conference | Genetic and Evolutionary Computation Conference (GECCO 2016) |
---|---|
Country/Territory | United States |
City | Denver, Colorado |
Period | 20.07.2016 → 24.07.2016 |
Internet address |
Keywords
- Procedural Content Generation
- Search-based Procedural Content Generation
- Novelty Search
- Genetic Algorithm
- HeuristicLab
- Genetic algorithm
- Heuristiclab
- Novelty search
- Search-based procedural content generation
- Procedural content generation