Multiple Heuristics Research Articles

Scheduling Prioritized Services in Multihop OFDMA Networks

Growing popularity of high-speed wireless broad-band access for real-time applications makes it increasingly relevant to study the admission control and scheduling of flows in a service differentiated manner. Next-generation wireless broadband networks employ orthogonal frequency division multiple access (OFDMA) technology that enables multiple users to communicate at the same time using a time-frequency grid. In this paper, we provide a mathematical model for prioritized admission control and scheduling in OFDMA-based multihop wireless networks. The problem is formulated as an integer linear program (ILP) that does joint admission control and scheduling of flows while satisfying its rate and latency requirements. We propose different heuristic algorithms for scheduling priority-based flows in centralized multihop OFDMA networks. We define the Flow Admittance (FA) metric and compare the different scheduling schemes based on this metric. Simulation results show that the Start from Frame Beginning (SFB) heuristic performs well in most of the scenarios. We also propose a combination approach that merges multiple heuristics. The FA values obtained from the combination approach are close to the ILP while incurring computation time orders of lower magnitude.

Monte Carlo hyper-heuristics for examination timetabling

Automating the neighbourhood selection process in an iterative approach that uses multiple heuristics is not a trivial task. Hyper-heuristics are search methodologies that not only aim to provide a general framework for solving problem instances at different difficulty levels in a given domain, but a key goal is also to extend the level of generality so that different problems from different domains can also be solved. Indeed, a major challenge is to explore how the heuristic design process might be automated. Almost all existing iterative selection hyper-heuristics performing single point search contain two successive stages; heuristic selection and move acceptance. Different operators can be used in either of the stages. Recent studies explore ways of introducing learning mechanisms into the search process for improving the performance of hyper-heuristics. In this study, a broad empirical analysis is performed comparing Monte Carlo based hyper-heuristics for solving capacitated examination timetabling problems. One of these hyper-heuristics is an approach that overlaps two stages and presents them in a single algorithmic body. A learning heuristic selection method (L) operates in harmony with a simulated annealing move acceptance method using reheating (SA) based on some shared variables. Yet, the heuristic selection and move acceptance methods can be separated as the proposed approach respects the common selection hyper-heuristic framework. The experimental results show that simulated annealing with reheating as a hyper-heuristic move acceptance method has significant potential. On the other hand, the learning hyper-heuristic using simulated annealing with reheating move acceptance (L–SA) performs poorly due to certain weaknesses, such as the choice of rewarding mechanism and the evaluation of utility values for heuristic selection as compared to some other hyper-heuristics in examination timetabling. Trials with other heuristic selection methods confirm that the best alternative for the simulated annealing with reheating move acceptance for examination timetabling is a previously proposed strategy known as the choice function.

The More, the Merrier: Combining Heuristic Estimators for Satisficing Planning

We empirically examine several ways of exploiting the information of multiple heuristics in a satisficing best-first search algorithm, comparing their performance in terms of coverage, plan quality, speed, and search guidance. Our results indicate that using multiple heuristics for satisficing search is indeed useful. Among the combination methods we consider, the best results are obtained by the alternation method of the "Fast Diagonally Downward" planner.

配送ルート最適化のための内部多世界GA

配送ルート最適化のための内部多世界GA

New feature selection algorithm based on multiple heuristics

In order to enhance the operation speed,reduce the memory space occupied and filter out irrelevant or low-degree relevant features,feature selection algorithm must be used.One novel feature selection algorithm named as selection method using multiple heuristic rules was presented.The algorithm comprehensively considered features,so features could be selected more effectively and accurately.Experimental results show that the algorithm is better than mutual information,chi-square statistic and document frequency that are the three best conventional feature selection measures.

A noniterative greedy algorithm for multiframe point correspondence

This paper presents a framework for finding point correspondences in monocular image sequences over multiple frames. The general problem of multiframe point correspondence is NP-hard for three or more frames. A polynomial time algorithm for a restriction of this problem is presented and is used as the basis of the proposed greedy algorithm for the general problem. The greedy nature of the proposed algorithm allows it to be used in real-time systems for tracking and surveillance, etc. In addition, the proposed algorithm deals with the problems of occlusion, missed detections, and false positives by using a single noniterative greedy optimization scheme and, hence, reduces the complexity of the overall algorithm as compared to most existing approaches where multiple heuristics are used for the same purpose. While most greedy algorithms for point tracking do not allow for entry and exit of the points from the scene, this is not a limitation for the proposed algorithm. Experiments with real and synthetic data over a wide range of scenarios and system parameters are presented to validate the claims about the performance of the proposed algorithm.

Multiple Heuristics and Their Combination for Automatic WordNet Mapping

This paper presents an automatic construction of Korean WordNet from pre-existing lexical resources. We develop a set of automatic word sense disambiguation techniques to link a Korean word sense collected from a bilingual machine-readable dictionary to a single corresponding English WordNet synset. We show how individual links provided by each word sense disambiguation method can be non-linearly combined to produce a Korean WordNet from existing English WordNet for nouns.

Scheduling Commercial Videotapes in Broadcast Television

This paper, motivated by the experiences of a major U.S.-based broadcast television network, presents algorithms and heuristics to schedule commercial videotapes. Major advertisers purchase several slots to air commercials during a given time period on a broadcast network. We study the problem of scheduling advertiser's commercials in the slots it purchased when the same commercial is to be aired multiple times. Under such a situation, the advertisers typically want the airings of a commercial to be as evenly spaced as possible. Thus, our objective is to schedule a set of commercials in a set of available slots such that multiple airings of the same commercial are as evenly spaced as possible. A natural formulation of this problem is a mixed-integer program that can be solved using third-party solvers. We also develop a branch-and-bound algorithm based on a problem-specific bounding scheme. Both approaches fail to solve larger problem instances within a reasonable time frame. We present an alternative mixed-integer program that lends itself to an efficient solution. For solving even larger problems, we present multiple heuristics.

An optimization model and multiple matching heuristics for quality planning in manufacturing systems

Planning quality control operations for parts assigned to a manufacturing system is an important decision-making process. It involves the selection of quality tools and assignment of the tools and quality operations to machine centers or inspection stations. The problem is complicated by multiple variables involved and the interactions between the variables. Trade-off must be made to minimize the total cost and satisfy demands and resource constraints. Mathematical formulation can be used to integrate the decisions and characterize the problem. More efficient heuristic algorithms are developed based on a tripartite graph representation of the original problem. Experimental results showed that the algorithms proposed are effective and efficient in their computational performance.

Team Learning of Computable Languages

A team of learning machines is a multiset of learning machines. A team is said to learn a concept successfully if each member of some nonempty subset, of predetermined size, of the team learns the concept. Team learning of languages may be viewed as a suitable theoretical model for studying computational limits on the use of multiple heuristics in learning from examples.

Algorithms to Select IDDQ Measurement Vectors for Bridging Faults in Sequential Circuits

In order to reduce IDDQ testing time, it is important to reduce the number of IDDQ measurement vectors, because IDDQ measurement is a time-consuming process. For obtaining minimum number of IDDQ measurement vectors for sequential circuits, fault simulation needs to be performed without fault-dropping, thus requiring very high simulation time. In this paper we propose algorithms to select small number of IDDQ measurement vectors. The proposed algorithms can concurrently simulate multiple faults and use heuristics for selection of IDDQ measurement vectors to reduce simulation time. Experimental results are presented to demonstrate the effectiveness of the proposed method.

The Influence of Heuristics on Psychological Science: A Case Study of Research on Creativity

Research heuristics—implicit rules used to guide work on a scientific problem—are explored for their role in guiding psychological research. Work on the psychology of creativity is used to illustrate how heuristics have guided research. We examine the influence of three heuristics: the trilogy‐of‐mind heuristic, the emotions‐as‐moods heuristic, and the analysis‐of‐variance heuristic. This analysis of multiple heuristics provides a new way to understand the state of research on creativity. In the discussion, the analysis is extended to other heuristics and to how heuristics may help as well as hinder innovative research.

Parallel search-and-learn techniques and graph coloring

The paper describes a parallel algorithm for graph coloring. The algorithm is based on a search-and-learn technique for combinatorial oprimization, where multiple heuristics are used to search nearly disjoint subspaces of the combinatorial search space, and the resulting solutions are analyzed for good decisions. A decision takes the form `color nodes i and j using the same color'. If a majority of the heuristics concur on coloring two nodes with the same color, the two nodes are merged into a supernode. Supernodes of more than two nodes can similarly be identified. The given graph can be collapsed into a smaller graph, the node set of which consists of supernodes. It is shown that an optimal coloring of the collapsed graph can be used to construct an optimal coloring of the original graph. Parallelism in the search-and-learn technique is prevalent in the multiple heuristic search phase, and it is easy to exploit using a multiprocessor or a multicomputer environment. In the implementation, a network of Sun workstations is used for parallelization. Experimental results are described for a number of large benchmark examples. The results show that the technique can deliver superlinear speedup and superlinear quality for large and difficult examples of node coloring.

Generalized best first search using single and multiple heuristics

Informed best first search is studied in a generalized framework in the setting of and/ or graphs. Strategies for effectively using single and multiple sources of heuristics in single and multipass algorithms are examined and analyzed. Specific polynomial time algorithms using these strategies are presented which are more efficient than known algorithms in terms of solution quality and worst case node expansions.

The psychology of moderate prediction: II. Leniency and uncertainty

In this paper we demonstrate that intuitive numerical predictions can be somewhat regressive. This moderation of predictions is asymmetric: predictions are more regressive at low than at high values of the predictor. This pattern is analyzed in terms of the operation of multiple heuristics. The representativeness heuristic is responsible for predictions in which extremity of the predicted variable is matched to extremity of the predictor. Matching is modified by a variety of intuitions that promote moderation per se; we lump these together under the heading of weak regressiveness. Third is leniency, a heuristic suggesting that the higher the uncertainty, the more positive should be the predictions. The first experiment demonstrates leniency in isolation from the other heuristics: in multivariate prediction, inconsistent predictors yield more positive predictions. Experiment 2 demonstrates asymmetric regression in a situation where all three heuristics are assumed to have effects. The third experiment exhibits leniency in the context of explanation of regression phenomena (rather than in numerical prediction). The final experiment explores the relation between the three heuristics and experience with multiple determination (Ganzach & Krantz, in press). It demonstrates increased moderation of predictions when subjects are required to generate a predicted value of an intermediate variable, for example, when the prediction of GPA from Intelligence is made subsequent to a prediction of Motivation.

Experimental evaluation of testability measures for test generation (logic circuits)

The results of an extensive study of five existing testability measures when used to aid heuristics for automatic test pattern generation algorithms are presented. Each measure was evaluated using over 60000 faults in circuits of varying size and complexity. The performance of these measures was rated using several different criteria. Based on these results the performance of a composite test generation strategy that uses multiple guidance heuristics was evaluated. The results indicate that this strategy not only provides better fault coverage but also reduces the average time taken to generate a test or determine that a given fault is undetectable. >