Topic
Incremental heuristic search
About: Incremental heuristic search is a research topic. Over the lifetime, 2376 publications have been published within this topic receiving 89502 citations.
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TL;DR: This paper presents tabu search, a heuristic procedure designed to guide other methods to escape the trap of local optimality, which has obtained optimal and near optimal solutions to a wide variety of classical and practical problems.
Abstract: Tabu search is a “higher level” heuristic procedure for solving optimization problems, designed to guide other methods (or their component processes) to escape the trap of local optimality. Tabu search has obtained optimal and near optimal solutions to a wide variety of classical and practical problems in applications ranging from scheduling to telecommunications and from character recognition to neural networks. It uses flexible structures memory (to permit search information to be exploited more thoroughly than by rigid memory systems or memoryless systems), conditions for strategically constraining and freeing the search process (embodied in tabu restrictions and aspiration criteria), and memory functions of varying time spans for intensifying and diversifying the search (reinforcing attributes historically found good and driving the search into new regions). Tabu search can be integrated with branch-and-bound and cutting plane procedures, and it has the ability to start with a simple implementation th...
1,040 citations
TL;DR: A family of heuristic search planners are studied based on a simple and general heuristic that assumes that action preconditions are independent, which is used in the context of best-first and hill-climbing search algorithms, and tested over a large collection of domains.
1,023 citations
TL;DR: A critical discussion of the scientific literature on hyper-heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas are presented.
Abstract: Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyper-heuristic is relatively new; it was first used in 2000 to describe heuristics to choose heuristics in the context of combinatorial optimisation. However, the idea of automating the design of heuristics is not new; it can be traced back to the 1960s. The definition of hyper-heuristics has been recently extended to refer to a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Two main hyper-heuristic categories can be considered: heuristic selection and heuristic generation. The distinguishing feature of hyper-heuristics is that they operate on a search space of heuristics (or heuristic components) rather than directly on the search space of solutions to the underlying problem that is being addressed. This paper presents a critical discussion of the scientific literature on hyper-heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas. Current research trends and directions for future research are also discussed.
1,023 citations
TL;DR: A variation of minimax lookahead search, and an analog to alpha-beta pruning that significantly improves the efficiency of the algorithm, and a new algorithm, called Real-Time-A∗, for interleaving planning and execution, which proves that the algorithm makes locally optimal decisions and is guaranteed to find a solution.
989 citations
TL;DR: In this article, the min-conflicts heuristic is used to minimize the number of constraint violations after each step in a value-ordering heuristic search, which can be used with a variety of different search strategies.
945 citations