Inductive functional programming using incremental program transformation
Artificial Intelligence
On the run-time behaviour of stochastic local search algorithms for SAT
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
The Definition of Standard ML
Tabu Search
A Heuristic for Boolean Optimization Problems
Journal of Heuristics
The complexity of theorem-proving procedures
STOC '71 Proceedings of the third annual ACM symposium on Theory of computing
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Computers and Industrial Engineering
Automated discovery of local search heuristics for satisfiability testing
Evolutionary Computation
Generating SAT local-search heuristics using a GP hyper-heuristic framework
EA'07 Proceedings of the Evolution artificielle, 8th international conference on Artificial evolution
Using automatic programming to generate state-of-the-art algorithms for random 3-SAT
Journal of Heuristics
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Local Search based meta-heuristic methods for finding good solutions to hard combinatorial optimization problems have attained a lot of success, and a plethora of methods exist, each with its own successes, and also with its own parameter settings and other method-specific details. At the same time, experience is needed to implement highly competitive code, and some of the experience applied is not easy to quantify.ADATE is a system to automatically generate code based on a set of input-output specifications, and can work in vastly different domains. It generates code in a subset of the programming language ML and works by searching for transformations of purely functional ML programs.Code automatically generated by the ADATE system compares with state-of-the-art handcrafted meta-heuristic optimization code. In particular, the programs generated by ADATE target the move selection part of BOOP--Boolean Optimization Problems. The baseline is a highly successful Tabu Search implementation. Comparisons are made for versions running for a limited number of iterations, being suitable for applications needing a short response time. The computational results show that the ADATE system is able to generate highly competitive code that produces more optimal solutions to hard BOOP instances within given iteration limits than the previously published Tabu Search implementation. The automatically generated code also gives new insights into the general design of meta-heuristic mechanisms, and contains novel search mechanisms.