Improved Knowledge Acquisition for High-Performance Heuristic Search

We present a new incremental knowledge acquisition approach that incrementally improves the performance of a probabilistic search algorithm. The approach addresses the known difficulty of tuning probabilistic search algorithms, such as genetic algorithms or simulated annealing, for a given search problem by the introduction of domain knowledge. We show that our approach is effective for developing heuristic algorithms for difficult combinatorial problems by solving benchmarks from the industrially relevant domain of VLSI detailed routing. In this paper we present advanced techniques for improving our knowledge acquisition approach. We also present a novel method that uses domain knowledge for the prioritisation of mutation operators, increasing the GA's efficiency noticeably.