Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A smart hill-climbing algorithm for application server configuration
Proceedings of the 13th international conference on World Wide Web
The Traveling Salesman Problem: A Computational Study (Princeton Series in Applied Mathematics)
The Traveling Salesman Problem: A Computational Study (Princeton Series in Applied Mathematics)
Optimal time-space tradeoff in probabilistic inference
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Combinatorial Optimization Using Electro-Optical Vector by Matrix Multiplication Architecture
OSC '09 Proceedings of the 2nd International Workshop on Optical SuperComputing
Time space tradeoffs in GA based feature selection for workload characterization
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
Improving the performance of constructive multi-start search using record-keeping
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
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Heuristic search techniques can often benefit from record keeping and saving of intermediate results, thereby improving performance through exploitation of time / space tradeoffs. Iterative hill climbing (ITHC) is one of these heuristics. This paper demonstrates that record keeping in the ITHC domain can significantly speed up the ITHC. The record keeping method is similar to the mechanism of a cache. The new approach is implemented and tested in the traveling salesperson search space. The research compares a traditional random restart (RR) procedure to a new greedy enumeration (GE) method. GE produces Hamiltonian-cycles that are about 10% shorter than the RR. Moreover, the cached RR achieves a speedup of 3x with a relatively small number of cities and only 20% with a medium number of cities (~17). The cached GE shows a highly significant speedup of 4x over traditional methods even with a relatively large number of cities (80).