82 citations · 191 across the 14 of their papers we have counts for
8 papers · 1 filter
Hybrid Encoding For Generating Large Scale Game Level Patterns With Local Variations
Jacob Schrum, Benjamin Capps, Kirby Steckel +2
Generative Adversarial Networks (GANs) are a powerful indirect genotype-to-phenotype mapping for evolutionary search. Much previous work applying GANs to level generation focuses o…
Identifying Properties of Real-World Optimisation Problems through a Questionnaire
Koen van der Blom, Timo M. Deist, Vanessa Volz +5
Optimisation algorithms are commonly compared on benchmarks to get insight into performance differences. However, it is not clear how closely benchmarks match the properties of rea…
Benchmarking in Optimization: Best Practice and Open Issues
Thomas Bartz-Beielstein, Carola Doerr, Daan van den Berg +14
This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world. Promoting best practice…
Towards Realistic Optimization Benchmarks: A Questionnaire on the Properties of Real-World Problems
Koen van der Blom, Timo M. Deist, Tea Tušar +5
Benchmarks are a useful tool for empirical performance comparisons. However, one of the main shortcomings of existing benchmarks is that it remains largely unclear how they relate…
CPPN2GAN: Combining Compositional Pattern Producing Networks and GANs for Large-scale Pattern Generation
Jacob Schrum, Vanessa Volz, Sebastian Risi
Generative Adversarial Networks (GANs) are proving to be a powerful indirect genotype-to-phenotype mapping for evolutionary search, but they have limitations. In particular, GAN ou…
Interactive Evolution and Exploration Within Latent Level-Design Space of Generative Adversarial Networks
Jacob Schrum, Jake Gutierrez, Vanessa Volz +3
Generative Adversarial Networks (GANs) are an emerging form of indirect encoding. The GAN is trained to induce a latent space on training data, and a real-valued evolutionary algor…
Tile Pattern KL-Divergence for Analysing and Evolving Game Levels
Simon M. Lucas, Vanessa Volz
This paper provides a detailed investigation of using the Kullback-Leibler (KL) Divergence as a way to compare and analyse game-levels, and hence to use the measure as the objectiv…
Surrogate-Assisted Partial Order-based Evolutionary Optimisation
Vanessa Volz, Günter Rudolph, Boris Naujoks
In this paper, we propose a novel approach (SAPEO) to support the survival selection process in multi-objective evolutionary algorithms with surrogate models - it dynamically choos…