CLEAR: Cue Learning using Evolution for Accurate Recognition Applied to Sustainability Data Extraction

Peter J. Bentley, Soo Ling Lim, Fuyuki Ishikawa·January 30, 2025

Summary

The CLEAR algorithm, employing LLMs and evolutionary computation, optimizes image recognition cues for tasks like building age, lighting, heating, window types, and energy consumption assessment. It surpasses human recognition and authored prompts, achieving significant accuracy improvements. CLEAR's variable-length representations and LLM consistency investigations highlight its real-world applicability, offering concise, coherent solutions.

Key findings

4

Introduction
Background
Overview of image recognition in energy assessment
Importance of accurate image recognition for tasks like building age, lighting, heating, window types, and energy consumption
Objective
To introduce the CLEAR algorithm, a novel approach that leverages Large Language Models (LLMs) and evolutionary computation for optimizing image recognition cues
Highlighting the algorithm's ability to surpass human recognition and authored prompts in achieving significant accuracy improvements
Method
Data Collection
Sources of image data for training and testing the CLEAR algorithm
Types of images used for various tasks (building age, lighting, heating, window types, energy consumption)
Data Preprocessing
Techniques employed for preparing the image data for the algorithm
Importance of data quality in enhancing the algorithm's performance
LLM Consistency Investigation
Analysis of the role of Large Language Models in the CLEAR algorithm
Examination of how LLM consistency impacts the algorithm's accuracy and efficiency
Variable-Length Representations
Explanation of how variable-length representations in the CLEAR algorithm facilitate its adaptability and effectiveness in diverse energy assessment tasks
Discussion on the benefits of this feature in real-world applications
Results
Accuracy Improvements
Quantitative and qualitative analysis of the CLEAR algorithm's performance compared to human recognition and authored prompts
Case studies demonstrating the algorithm's superiority in various energy assessment tasks
Real-World Applicability
Overview of the CLEAR algorithm's implementation in real-world scenarios
Discussion on the algorithm's potential impact on energy efficiency and sustainability initiatives
Conclusion
Summary of Contributions
Recap of the CLEAR algorithm's innovative features and its contributions to the field of image recognition for energy assessment
Future Directions
Potential areas for further research and development of the CLEAR algorithm
Considerations for scaling and integrating the algorithm into broader energy management systems
Basic info
papers
neural and evolutionary computing
computer vision and pattern recognition
artificial intelligence
Advanced features
Insights
How does the CLEAR algorithm utilize LLMs and evolutionary computation?
What is the main purpose of the CLEAR algorithm?
What tasks does the CLEAR algorithm optimize image recognition cues for?
What are some of the key features that highlight the real-world applicability of the CLEAR algorithm?