Development of direct-search strategies in hill-climbing problems
نویسندگان
چکیده
منابع مشابه
Combinatorial Auctions, Knapsack Problems, and Hill-Climbing Search
This paper examines the performance of hill climbing algo rithms on standard test problems for combinatorial auctions CAs On single unit CAs deterministic hill climbers are found to perform well and their performance can be improved signi cantly by randomizing them and restarting them several times or by using them collectively For some problems this good performance is shown to be no better th...
متن کاملCombinatorial Auctions , Knapsack Problems , and Hill - climbing
This paper examines the performance of hill-climbing algorithms on standard test problems for combinatorial auctions (CAs). On single-unit CAs, deterministic hill-climbers are found to perform well, and their performance can be improved signiicantly by randomizing them and restarting them several times, or by using them collectively. For some problems this good performance is shown to be no bet...
متن کاملHill-climbing Search in Evolutionary Models for Protein Folding Simulations
Evolutionary algorithms and hill-climbing search models are investigated to address the protein structure prediction problem. This is a well-known NP-hard problem representing one of the most important and challenging problems in computational biology. The pull move operation is engaged as the main local search operator in several approaches to protein structure prediction. The considered appro...
متن کاملComplete Local Search: Boosting Hill-Climbing through Online Relaxation Refinement
Several known heuristic functions can capture the input at different levels of precision, and support relaxation-refinement operations guaranteeing to converge to exact information in a finite number of steps. A natural idea is to use such refinement online, during search, yet this has barely been addressed. We do so here for local search, where relaxation refinement is particularly appealing: ...
متن کاملIncremental Hill-Climbing Search Applied to Bayesian Network Structure Learning
We propose two general heuristics to transform a batch Hillclimbing search into an incremental one. Then, we apply our heuristics to two Bayesian network structure learning algorithms and experimentally see that our incremental approach saves a significant amount of computing time while it yields similar networks than the batch algorithms.
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ژورنال
عنوان ژورنال: Bulletin of the Psychonomic Society
سال: 1983
ISSN: 0090-5054
DOI: 10.3758/bf03330011