Heuristic sampling: a method for predicting the performance of tree searching programs
SIAM Journal on Computing
Random DFA's can be approximately learned from sparse uniform examples
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Inductive Inference: Theory and Methods
ACM Computing Surveys (CSUR)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
Inducing grammars from sparse data sets: a survey of algorithms and results
The Journal of Machine Learning Research
Learning Deterministic Finite Automata with a Smart State Labeling Evolutionary Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Beam-ACO Based on Stochastic Sampling for Makespan Optimization Concerning the TSP with Time Windows
EvoCOP '09 Proceedings of the 9th European Conference on Evolutionary Computation in Combinatorial Optimization
Incomplete tree search using adaptive probing
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Beam-ACO for the travelling salesman problem with time windows
Computers and Operations Research
Software model synthesis using satisfiability solvers
Empirical Software Engineering
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In the field of Operation Research and Artificial Intelligence, several stochastic search algorithms have been designed based on the theory of global random search (Zhigljavsky 1991). Basically, those techniques iteratively sample the search space with respect to a probability, distribution which is updated according to the result of previous samples and some predefined strategy. Genetic Algorithms (GAs) (Goldberg 1989) or Greedy Randomized Adaptive Search Procedures (GRASP) (Feo & Resende 1995) are two particular instances of this paradigm. In this paper, we present SAGE, a search algorithm based on the same fundamental mechanisms as those techniques. However, it addresses a class of problems for which it is difficult to design transformation operators to perform local search because of intrinsic constraints in the definition of the problem itself. For those problems, a procedural approach is the natural way to construct solutions, resulting in a state space represented as a tree or a DAG. The aim of this paper is to describe the underlying heuristics used by SAGE to address problems belonging to that class. The performance of SAGE is analyzed on the problem of grammar induction and its successful application to problems from the recent Abbadingo DFA learning competition is presented.