Particle Swarm Optimization (PSO) is a popular gradient-free meta-heuristic, but its many variants pose significant selection and tuning challenges for non-specialists. We introduce Automated Deep-learning-based Particle Swarm Optimization (AD-PSO), a framework that highly automates PSO configuration and execution. AD-PSO features a modular design, incorporating diverse PSO variants as interchangeable components. The core methodology involves training a hard-constrained Conditional Variational Autoencoder (CVAE) to map problem landscape features—obtained via Latin Hypercube Sampling (LHS)—to a near-optimal PSO configuration. This mapping is established a priori through meta-optimization. The user only specifies the maximum number of evaluations; AD-PSO then autonomously samples the function, computes its features, and uses the trained CVAE to generate and execute a tailored configuration. Experimental results demonstrate AD-PSO's strong adaptability. In 297 comparisons on the CEC2022 benchmark against 9 state-of-the-art competitors, AD-PSO won or tied in 239 cases (an 80.5% win/draw rate).
Yutong Zou, Peter J. Bentley· Annual Conference on Genetic...· 0 citations
The overtake car maneuver involves high risk and complex judgement. For autonomous vehicles this is challenging, especially for human-initiated overtake requests. If a user requests the maneuver there must be a rapid safety assessment. Language models have great potential to classify safety with explanations, but they struggle to disentangle critical information from complex vehicular environments. We apply CLEAR (Cue Learning using Evolution for Accurate Recognition) to evolve prompt cues that optimize the ability of language models to correctly predict safety scores for overtaking maneuvers. To achieve this, we create a novel open-source symbolic traffic model EvoDrive designed specifically for EC research, which outputs LLM-readable snapshots. We show that LLMs + EvoDrive with CLEAR can reduce error by more than 20% compared to without CLEAR, with statistically significant results. Analysis shows evolved cues are coherent and have reduced variability in LLM output.
Peter J. Bentley, S. Lim, Fuyuki Ishikawa et al.· Annual Conference on Genetic...· 0 citations