Learning Effective Search Heuristics
نویسنده
چکیده
SAGE.2 is a production system that improves its search strategies with practice. The program incorporates four different heuristics for assigning credit and blame, and employs a discrimination process to direct its search through the space of move-proposing rules. The system has shown its generality by learning search heuristics in five different task domains. In addition to improving its search behavior on practice problems, SAGE.2 was able to transfer its expertise to scaled-up versions of a task, and in one case transferred its acquired search strategy to problems with different initial and goal states.
منابع مشابه
An integrated approach for scheduling flexible job-shop using teaching–learning-based optimization method
In this paper, teaching–learning-based optimization (TLBO) is proposed to solve flexible job shop scheduling problem (FJSP) based on the integrated approach with an objective to minimize makespan. An FJSP is an extension of basic job-shop scheduling problem. There are two sub problems in FJSP. They are routing problem and sequencing problem. If both the sub problems are solved simultaneously, t...
متن کاملLearning a Mixture of Search Heuristics
Problem solvers have at their disposal many heuristics that may support effective search. The efficacy of these heuristics, however, varies with the problem class, and their mutual interactions may not be well understood. The long-term goal of our work is to learn how to select appropriately from among a large body of heuristics, and how to combine them into a weighted mixture that works well o...
متن کاملHyperILS: An Effective Iterated Local Search Hyper-heuristic for Combinatorial Optimisation
Two powerful ideas from search methodologies, iterated local search and hyperheuristics, are combined into a simple and effective framework to solve combinatorial optimisation problems (HyperILS). Iterated local search is a simple but successful algorithm. It operates by iteratively alternating between applying a move operator to the incumbent solution and restarting local search from the pertu...
متن کاملEffective heuristics and meta-heuristics for the quadratic assignment problem with tuned parameters and analytical comparisons
Quadratic assignment problem (QAP) is a well-known problem in the facility location and layout. It belongs to the NP-complete class. There are many heuristic and meta-heuristic methods, which are presented for QAP in the literature. In this paper, we applied 2-opt, greedy 2-opt, 3-opt, greedy 3-opt, and VNZ as heuristic methods and tabu search (TS), simulated annealing, and pa...
متن کاملReinforced Adaptive Large Neighborhood Search
The Large Neighborhood Search metaheuristic for solving Constrained Optimization Problems has been proved to be effective on a wide range of problems. This Local Search heuristic has the particularity of using a complete search (such as Constraint Programming) to explore the large neighborhoods obtained by relaxing a fragment of the variables of the current solution. Large Neighborhood Search h...
متن کاملAn Empirical Comparison of Hill-Climbing and Exhaustive Search in Inductive Rule Learning An Empirical Comparison of Hill-Climbing and Exhaustive Search in Inductive Rule Learning
Most commonly used inductive rule learning algorithms employ a hill-climbing search, whereas local pattern discovery algorithms employ exhaustive search. In this paper, we evaluate the spectrum of different search strategies to see whether separate-and-conquer rule learning algorithms are able to gain performance in terms of predictive accuracy or theory size by using more powerful search strat...
متن کامل