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Results 11 - 20
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1,999
DFS-Tree Based Heuristic Search ⋆
"... Abstract. In constraint satisfaction, local search is an incomplete method for finding a solution to a problem. Solving a general constraint satisfaction problem (CSP) is known to be NP-complete; so that heuristic techniques are usually used. The main contribution of this work is twofold: (i) a tech ..."
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Cited by 1 (1 self)
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technique for de-composing a CSP into a DFS-tree CSP structure; (ii) an heuristic search technique for solving DFS-tree CSP structures. This heuristic search technique has been empirically evaluated with random CSPs. The evaluation results show that the behavior of our heuristic outperforms than
Meta-Learning Rule Learning Heuristics Meta-Learning Rule Learning Heuristics
"... The goal of this paper is to investigate to what extent a rule learning heuristic can be learned from experience. Our basic approach is to learn a large number of rules and record their performance on the test set. Subsequently, we train regression algorithms on predicting the test set performance f ..."
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heuristics outperform standard rule learning heuristics. We also analyze their behavior in coverage space. 1.
Meta-learning rule learning heuristics
- Technical Report. Knowledge Engineering Group
, 2007
"... The goal of this paper is to investigate to what extent a rule learning heuristic can be learned from experience. To that end, we let a rule learner learn a large number of rules and record their performance on the test set. Subsequently, we train regression algorithms on predicting the test set per ..."
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Cited by 10 (7 self)
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show that the learned heuristics outperform standard rule learning heuristics. We also analyze their behavior in coverage space. 1
New Dynamic Heuristics in the Client-Agent-Server Model
- in "IEEE Heterogeneous Computing Workshop - HCW’03
, 2003
"... MCT is a widely used heuristic for scheduling tasks onto grid platforms. However, when dealing with many tasks, MCT tends to dramatically delay already mapped task completion time, while scheduling a new task. In this paper we propose heuristics based on two features: the historical trace manager th ..."
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Cited by 5 (1 self)
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that simulates the environment and the perturbation that defines the impact a new allocated task has on already mapped tasks. Our simulations and experiments on a real environment show that the proposed heuristics outperform MCT.
A New Heuristic for Improving the Performance of Genetic Algorithm
"... Abstract—The hybridisation of genetic algorithm with heuristics has been shown to be one of an effective way to improve its performance. In this work, genetic algorithm hybridised with four heuristics including a new heuristic called neighbourhood improvement were investigated through the classical ..."
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Cited by 3 (0 self)
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travelling salesman problem. The experimental results showed that the proposed heuristic outperformed other heuristics both in terms of quality of the results obtained and the computational time.
Scalable Influence Maximization for Prevalent Viral Marketing in Large-Scale Social Networks
"... Influence maximization, defined by Kempe, Kleinberg, and Tardos (2003), is the problem of finding a small set of seed nodes in a social network that maximizes the spread of influence under certain influence cascade models. The scalability of influence maximization is a key factor for enabling preval ..."
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Cited by 183 (14 self)
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in influence spread — it is always among the best algorithms, and in most cases it significantly outperforms all other scalable heuristics to as much as 100%–260 % increase in influence spread.
An Improved Heuristic for Minimizing Total Completion Time on Single Machine with Release Time
, 2009
"... Abstract: This study considers the scheduling problem of minimizing the total completion time on a single machine with release dates. In view of the NP-Hard nature of the problem, one heuristic (called MM) was proposed for this problem. The proposed heuristic and two others (AEO and HR1) selected f ..."
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from the literature were tested on a total of 450 randomly generated single machine scheduling problems ranging from 3 to 500 jobs. Experimental results show that the proposed heuristic outperforms the AEO algorithm with respect to effectiveness and efficiency. Also, the proposed heuristic outperforms
An Optimizationbased Heuristic for Maximal Constraint Satisfaction
- In Proceedings of International Conference on Principles and Practice of Constraint Programming
, 1995
"... Abstract. We present a new heuristic approach for maximal constraint satisfaction of overconstrained problems (MAX-CSP). This approach is based on a formulation of CSP as an optimization problem presented in a previous paper [Meseguer and Larrosa, 95], which has given good results on some classes o ..."
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Cited by 15 (2 self)
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updating formula to avoid redundant computation. We have tested both heuristics with the P-EFC3 algorithm on several instances of two classes of random CSP. Experimental results show that both heuristics outperform previously used heuristics based on inconsistency counts. In fact, the lowest support
A New Heuristic Approach for the Stock- Cutting Problems
"... Abstract—This paper addresses a stock-cutting problem with rotation of items and without the guillotine cutting constraint. In order to solve the large-scale problem effectively and efficiently, we propose a simple but fast heuristic algorithm. It is shown that this heuristic outperforms the latest ..."
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Abstract—This paper addresses a stock-cutting problem with rotation of items and without the guillotine cutting constraint. In order to solve the large-scale problem effectively and efficiently, we propose a simple but fast heuristic algorithm. It is shown that this heuristic outperforms the latest
Unifying the causal graph and additive heuristics
- Proceedings of the 18th International Conference on Automated Planning and Scheduling (ICAPS
, 2008
"... Many current heuristics for domain-independent planning, such as Bonet and Geffner’s additive heuristic and Hoffmann and Nebel’s FF heuristic, are based on delete relaxations. They estimate the goal distance of a search state by approximating the solution cost in a relaxed task where negative conseq ..."
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Cited by 56 (14 self)
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and the additive heuristic and outperforms them across a wide range of standard benchmarks.
Results 11 - 20
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1,999
