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cloud.google.com·6h ago
AI Research
arXiv CS.AI·27d ago·5 min read
Surrogate-Assisted Genetic Programming with Phenotypic Characterisation in Dynamic Multi-Mode Project Scheduling
TL;DR
Surrogate-assisted genetic programming (GP) was developed to enhance scheduling heuristics in dynamic multi-mode project scheduling. The study found that binary encoding with Euclidean distance significantly improves fitness estimation and evolutionary search efficiency.
✦ Why It Matters
Implement surrogate-assisted GP with binary encoding to enhance your project scheduling heuristics today.
Key Takeaways
- Explore surrogate-assisted GP for complex scheduling problems.
- Utilize binary encoding for effective fitness estimation.
- Incorporate Euclidean distance to measure behavioral similarity.
- Combine duplicate removal with surrogate preselection for better results.
How It Works
The study employs surrogate-assisted genetic programming to evolve scheduling heuristics by encoding GP individuals into phenotypic characterisations. Three encoding schemes were tested: priority-value encoding retains raw outputs, rank encoding captures order, and binary encoding represents final decisions.
The binary encoding, when paired with Euclidean distance, effectively estimates fitness and reduces redundancy, allowing for a more efficient evolutionary search.