Discovering new robust local search algorithms with neuro-evolution
Mohamed Salim Amri Sakhri, Adrien Goëffon, Olivier Goudet, Frédéric Saubion, Chaïmaâ Touhami·January 08, 2025
Summary
The paper introduces a novel neuro-evolution approach to enhance local search algorithms. It proposes using neural networks with similar input to conventional algorithms for better decision-making. The study evaluates different information representations for efficiency and robustness in solving black-box problems, particularly through NK landscape problems. This method promises to generate more effective and adaptable local search algorithms. The Neuro-LS algorithm adapts its strategy based on the landscape's ruggedness, learning adaptive policies through a neural network. It outperforms basic deterministic local search methods, offering superior results through an innovative worst-case improvement strategy. The algorithm's emergent strategies, based on move ranks, prove robust and versatile, applicable across different problem sizes and pseudo-Boolean challenges.
Introduction
Background
Overview of local search algorithms
Challenges in solving black-box problems
Role of neural networks in optimization
Objective
To introduce a novel neuro-evolution approach for improving local search algorithms
Focus on using neural networks for better decision-making in local search contexts
Method
Data Collection
Gathering black-box problem instances, particularly NK landscape problems
Data Preprocessing
Preparing data for neural network training
Feature extraction for efficient representation
Neuro-LS Algorithm
Algorithm Design
Integration of neural networks with local search strategies
Use of neural networks for adaptive policy learning
Implementation Details
Architecture of the neural network
Training process and parameter tuning
Strategy Adaptation
Learning to adjust search strategies based on landscape ruggedness
Incorporating worst-case improvement strategies
Evaluation
Efficiency and Robustness
Testing across various problem sizes and pseudo-Boolean challenges
Comparison with basic deterministic local search methods
Performance Metrics
Quantitative analysis of solution quality and speed
Statistical significance of improvements
Results
Comparative Analysis
Detailed outcomes of Neuro-LS against conventional algorithms
Success rates and efficiency gains
Case Studies
Illustrative examples demonstrating algorithm effectiveness
Insights into emergent strategies based on move ranks
Conclusion
Summary of Contributions
Recap of the novel neuro-evolution approach
Impact on local search algorithm performance
Future Directions
Potential areas for further research
Applications in broader optimization contexts
Basic info
papers
neural and evolutionary computing
artificial intelligence
Advanced features
Insights
What makes the Neuro-LS algorithm's emergent strategies based on move ranks robust and versatile?
How does the Neuro-LS algorithm adapt its strategy based on the landscape's characteristics?
What type of problems does the study evaluate the Neuro-LS algorithm on, and what are the results compared to?