26 citations · 78 across the 13 of their papers we have counts for
5 papers · 1 filter
A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning
Naoki Masuyama, Takanori Takebayashi, Yusuke Nojima +3
In general, a similarity threshold (i.e., a vigilance parameter) for a node learning process in Adaptive Resonance Theory (ART)-based algorithms has a significant impact on cluster…
Reference Vector Adaptation and Mating Selection Strategy via Adaptive Resonance Theory-based Clustering for Many-objective Optimization
Takato Kinoshita, Naoki Masuyama, Yiping Liu +2
Decomposition-based multiobjective evolutionary algorithms (MOEAs) with clustering-based reference vector adaptation show good optimization performance for many-objective optimizat…
Effects of Different Optimization Formulations in Evolutionary Reinforcement Learning on Diverse Behavior Generation
Victor Villin, Naoki Masuyama, Yusuke Nojima
Generating various strategies for a given task is challenging. However, it has already proven to bring many assets to the main learning process, such as improved behavior explorati…
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 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…