Tournament versus Fitness Uniform Selection
Shane Legg, Marcus Hutter, Akshat Kumar
Abstract
In evolutionary algorithms a critical parameter that must be tuned is that of selection pressure. If it is set too low then the rate of convergence towards the optimum is likely to be slow. Alternatively if the selection pressure is set too high the system is likely to become stuck in a local optimum due to a loss of diversity in the population. The recent Fitness Uniform Selection Scheme (FUSS) is a conceptually simple but somewhat radical approach to addressing this problem - rather than biasing the selection towards higher fitness, FUSS biases selection towards sparsely populated fitness levels. In this paper we compare the relative performance of FUSS with the well known tournament selection scheme on a range of problems.
Create a lesson
Related papers
Calibrated RF-Fingerprinting Under Interference With Heterogeneous Transmission Protocols
Tariq Abdul-Quddoos, Xiangfang Li, Lijun Qian
Video DeltaNet: A Video-Native Hybrid Attention for Livestream Video Generation
Haocheng Xi, Yiming Xie, Hexu Zhao et al.
Don't Mask the Environment: Observation Supervision Changes How Agents Explore Under RL
Juzheng Zhang, Disha Makhija, Manoj Ghuhan Arivazhagan et al.
RISC-V and machine learning: a survey
Shriman Keshri, Apparna Singh, Chinmaya Kumar Palo et al.
Epidemiological Causal Graph Identification: Challenges, Identifiability and Algorithms
Sambit Mishra, Yingying Wang, Christine K. Johnson et al.
Multi-center Medical Data Mining with FL-Net - A One-stop Shop for Federated Learning
Simon Süwer, Julian Klemm, Elisa Acitelli et al.