Combinatorial algorithms for integrated circuit layout
Combinatorial algorithms for integrated circuit layout
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Theoretical basis for hierarchical incremental knowledge acquisition
International Journal of Human-Computer Studies
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Representations for Genetic and Evolutionary Algorithms
Representations for Genetic and Evolutionary Algorithms
Using Genetic Algorithms to Solve NP-Complete Problems
Proceedings of the 3rd International Conference on Genetic Algorithms
Knowledge in Context: A Strategy for Expert System Maintenance
AI '88 Proceedings of the 2nd Australian Joint Artificial Intelligence Conference
Extraction and reuse of design patterns from genetic algorithms using case-based reasoning
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Situated Cognition in the Semantic Web Era
EKAW '08 Proceedings of the 16th international conference on Knowledge Engineering: Practice and Patterns
Two decades of ripple down rules research
The Knowledge Engineering Review
Incremental knowledge acquisition using generalised RDR for soccer simulation
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
Incremental system engineering using process networks
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
Ripple down rules for vietnamese named entity recognition
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
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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.