82 citations · 191 across the 14 of their papers we have counts for
7 papers · 1 filter
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…
Towards Game-Playing AI Benchmarks via Performance Reporting Standards
Vanessa Volz, Boris Naujoks
While games have been used extensively as milestones to evaluate game-playing AI, there exists no standardised framework for reporting the obtained observations. As a result, it re…
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…
Capturing Local and Global Patterns in Procedural Content Generation via Machine Learning
Vanessa Volz, Niels Justesen, Sam Snodgrass +5
Recent procedural content generation via machine learning (PCGML) methods allow learning from existing content to produce similar content automatically. While these approaches are…
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…