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Wilt, Christopher, and Wheeler Ruml. "Effective Heuristics for Suboptimal Best-First Search." Journal of Artificial Intelligence Research 57 (October 31, 2016): 273–306. http://dx.doi.org/10.1613/jair.5036.
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Suboptimal heuristic search algorithms such as weighted A* and greedy best-first search are widely used to solve problems for which guaranteed optimal solutions are too expensive to obtain. These algorithms crucially rely on a heuristic function to guide their search. However, most research on building heuristics addresses optimal solving. In this paper, we illustrate how established wisdom for constructing heuristics for optimal search can fail when considering suboptimal search. We consider the behavior of greedy best-first search in detail and we test several hypotheses for predicting when a heuristic will be effective for it. Our results suggest that a predictive characteristic is a heuristic's goal distance rank correlation (GDRC), a robust measure of whether it orders nodes according to distance to a goal. We demonstrate that GDRC can be used to automatically construct abstraction-based heuristics for greedy best-first search that are more effective than those built by methods oriented toward optimal search. These results reinforce the point that suboptimal search deserves sustained attention and specialized methods of its own.
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Cichowicz, T., M. Drozdowski, M. Frankiewicz, G. Pawlak, F. Rytwinski, and J. Wasilewski. "Hyper-heuristics for cross-domain search." Bulletin of the Polish Academy of Sciences: Technical Sciences 60, no. 4 (December 1, 2012): 801–8. http://dx.doi.org/10.2478/v10175-012-0093-7.
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Abstract In this paper we present two hyper-heuristics developed for the Cross-Domain Heuristic Search Challenge. Hyper-heuristics solve hard combinatorial problems by guiding low level heuristics, rather than by manipulating problem solutions directly. Two hyper-heuristics are presented: Five Phase Approach and Genetic Hive. Development paths of the algorithms and testing methods are outlined. Performance of both methods is studied. Useful and interesting experience gained in construction of the hyper-heuristics are presented. Conclusions and recommendations for the future advancement of hyper-heuristic methodologies are discussed.
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Abdul-Razaq, Tariq, Hanan Chachan, and Faez Ali. "Modified Heuristics for Scheduling in Flow Shop to Minimize Makespan." Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 ), no. 2 (October 19, 2021): 1–20. http://dx.doi.org/10.55562/jrucs.v30i2.361.
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The NP-completeness of flow shops scheduling problems has been discussed for many years. Hence many heuristics have been proposed to obtain solutions of good quality with a small computational effort. The CDS (Campbell et al) and NEH (Nawaz, Enscore and Ham) heuristics are efficient among meta-heuristics such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).This paper discusses some methods and suggests new developing to the methods of the scheduling in flow shop to minimize makespan problems. Our main object in this paper, from one side, is to improve efficient heuristics which will be better than the existing heuristics given in the literature and yield solutions within a short time like Simple Heuristic Methods (SHM) and the First Heuristic Decreasing Arrange (DR). From other side, we apply two local search methods like GA and PSO algorithms on flow shop problems.Experimental analysis has been given of the performance of the proposed heuristics and local search methods with the relative efficient existing heuristics.
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Shanklin, Roslyn, Philip Kortum, and Claudia Ziegler Acemyan. "Adaptation of Heuristic Evaluations for the Physical Environment." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no. 1 (December 2020): 1135–39. http://dx.doi.org/10.1177/1071181320641272.
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Previous work has investigated the need for domain specific heuristics. Nielsen’s ten heuristics offer a general list of principles, but those principles may not capture usability issues specific to a given interface. Studies have demonstrated methods to establish a domain specific heuristic set, but very little research has been conducted on interfaces in the physical environment, creating a gap in the state-of-the-art. The research described in this paper aims to address this gap by developing an environmental heuristic set; the heuristic set was developed specifically for the Houston light rail system, METRORail. Following development, the heuristic set was validated against Nielsen’s more general heuristics through several field studies. Results highlighted that there were significantly more usability issues identified when using the environment-based heuristics than the general heuristics. This suggests that domain specific heuristics provide a framework that allows evaluators to capture usability issues particular to the interface of the physical environment.
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Triki, C. "Solution Methods for the Periodic Petrol Station Replenishment Problem." Journal of Engineering Research [TJER] 10, no. 2 (December 1, 2013): 69. http://dx.doi.org/10.24200/tjer.vol10iss2pp69-77.
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In this paper we introduce the Periodic Petrol Station Replenishment Problem (PPSRP) over a T-day planning horizon and describe four heuristic methods for its solution. Even though all the proposed heuristics belong to the common partitioning-then-routing paradigm, they differ in assigning the stations to each day of the horizon. The resulting daily routing problems are then solved exactly until achieving optimalization. Moreover, an improvement procedure is also developed with the aim of ensuring a better quality solution. Our heuristics are tested and compared in two real-life cases, and our computational results show encouraging improvements with respect to a human planning solution
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Gonzalez-Holland, Emily, Daphne Whitmer, Larry Moralez, and Mustapha Mouloua. "Examination of the Use of Nielsen’s 10 Usability Heuristics & Outlooks for the Future." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1 (September 2017): 1472–75. http://dx.doi.org/10.1177/1541931213601853.
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Heuristics are commonly employed throughout various stages of the design process to evaluate the usability of interfaces. Heuristic Evaluation (HE) provides researchers with a cost effective and practical means to effectively assess designs. In this article, we aim to outline the development and application of one of the most frequently cited set of heuristic evaluation tools, Nielsen’s (1994) 10 usability heuristics. Nielsen’s heuristics have not only been applied to various modalities of interface design, but have also been compared to other usability evaluation methods. Moreover, in many cases they have been modified so that they can be applied in an ever-changing socio-technical environment. In reviewing these developments, we propose theoretical and practical implications of these heuristic methods and present an outlook for the future. We argue that with the rapid expansion and growth of technology in the last 20 years, Nielsen’s 10 usability heuristics may need an update to remain consistent with modern usability problems.
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CHANG, KAI H., JAMES H. CROSS II, W. HOMER CARLISLE, and SHIH-SUNG LIAO. "A PERFORMANCE EVALUATION OF HEURISTICS-BASED TEST CASE GENERATION METHODS FOR SOFTWARE BRANCH COVERAGE." International Journal of Software Engineering and Knowledge Engineering 06, no. 04 (December 1996): 585–608. http://dx.doi.org/10.1142/s0218194096000247.
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Software testing is an important step in the development of complex systems. The construction of test cases using traditional methods usually requires considerable manual effort. QUEST/Ada—Query Utility Environment for Software Testing of Ada, is a prototype test case generation system that uses various heuristics-based approaches to generate test cases. The system, which is designed for unit testing, generates test cases by monitoring the branch coverage progress and intelligently modifying existing test cases to achieve additional coverage. Three heuristics-based approaches along with a random test case generation method were studied to compare their branch coverage performance. Although some constraints are imposed by the prototype, the framework provides a useful foundation for further heuristics-based test case generation research. The design of the system, the heuristic rules used in the system, and an evaluation of each rule’s performance are presented.
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Парфентьєва І. П. and Матвійчук К. О. "ЕТИМОЛОГІЯ ПОНЯТТЯ “ЕВРИСТИЧНИЙ ПІДХІД”." World Science 3, no. 8(36) (August 30, 2018): 46–49. http://dx.doi.org/10.31435/rsglobal_ws/30082018/6078.
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The article deals with the term "heuristics", which in scientific knowledge has the designation as a "method of finding." The author conducts an analysis of scientific literature on the interpretation of heuristics as a scientific concept. Heuristics as an independent science has not been fully formed yet. Despite the large number of scientific papers devoted to questions of heuristics, they usually relate to its particular problems and do not give a clear idea of either the object or subject of heuristics or its status among other sciences. It is suggested that pedagogical heuristics are defined as one of the branches of heuristics, which studies fundamental principles of didactics and the systematic organization of students' heuristic activity in a supervised interaction of a teacher and implements them in general and in vocational education using the system of problem tasks for the purposeful development of intelligence. The main tasks of pedagogical heuristics interact with the tasks of didactics in the qualitative formation of a future specialist. Therefore, pedagogical heuristics in researches use heuristic methods that allow to accelerate the process of solving any problem.
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Soria-Alcaraz, Jorge A., Gabriela Ochoa, Andres Espinal, Marco A. Sotelo-Figueroa, Manuel Ornelas-Rodriguez, and Horacio Rostro-Gonzalez. "A Methodology for Classifying Search Operators as Intensification or Diversification Heuristics." Complexity 2020 (February 13, 2020): 1–10. http://dx.doi.org/10.1155/2020/2871835.
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Selection hyper-heuristics are generic search tools that dynamically choose, from a given pool, the most promising operator (low-level heuristic) to apply at each iteration of the search process. The performance of these methods depends on the quality of the heuristic pool. Two types of heuristics can be part of the pool: diversification heuristics, which help to escape from local optima, and intensification heuristics, which effectively exploit promising regions in the vicinity of good solutions. An effective search strategy needs a balance between these two strategies. However, it is not straightforward to categorize an operator as intensification or diversification heuristic on complex domains. Therefore, we propose an automated methodology to do this classification. This brings methodological rigor to the configuration of an iterated local search hyper-heuristic featuring diversification and intensification stages. The methodology considers the empirical ranking of the heuristics based on an estimation of their capacity to either diversify or intensify the search. We incorporate the proposed approach into a state-of-the-art hyper-heuristic solving two domains: course timetabling and vehicle routing. Our results indicate improved performance, including new best-known solutions for the course timetabling problem.
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West, Todd, John Sessions, and Bogdan M. Strimbu. "Heuristic Optimization of Thinning Individual Douglas-Fir." Forests 12, no. 3 (February 28, 2021): 280. http://dx.doi.org/10.3390/f12030280.
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Research Highlights: (1) Optimizing mid-rotation thinning increased modeled land expectation values by as much as 5.1–10.1% over a representative reference prescription on plots planted at 2.7 and 3.7 m square spacings. (2) Eight heuristics, five of which were newly applied to selecting individual trees for thinning, produced thinning prescriptions of near identical quality. (3) Based on heuristic sampling properties, we introduced a variant of the hero heuristic with a 5.3–20% greater computational efficiency. Background and Objectives: Thinning, which is arguably the most subjective human intervention in the life of a stand, is commonly executed with limited decision support in tree selection. This study evaluated heuristics’ ability to support tree selection in a factorial experiment that considered the thinning method, tree density, thinning age, and rotation length. Materials and Methods: The Organon growth model was used for the financial optimization of even age Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) harvest rotations consisting of a single thinning followed by clearcutting on a high-productivity site. We evaluated two versions of the hero heuristic, four Monte Carlo heuristics (simulated annealing, record-to-record travel, threshold accepting, and great deluge), a genetic algorithm, and tabu search for their efficiency in maximizing land expectation value. Results: With 50–75 years rotations and a 4% discount rate, heuristic tree selection always increased land expectation values over other thinning methods. The two hero heuristics were the most computationally efficient methods. The four Monte Carlo heuristics required 2.8–3.4 times more computation than hero. The genetic algorithm and the tabu search required 4.2–8.4 and 21–52 times, respectively, more computation than hero. Conclusions: The accuracy of the resulting thinning prescriptions was limited by the quality of stand measurement, and the accuracy of the growth and yield models was linked to the heuristics rather than to the choice of heuristic. However, heuristic performance may be sensitive to the chosen models.
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Okitonyumbe Y. F, Joseph, Berthold Ulungu E.-L, and Joel Kapiamba Nt. "Cobweb heuristic for multi-objective vehicle routing problem." International Journal of Applied Mathematical Research 4, no. 3 (July 11, 2015): 430. http://dx.doi.org/10.14419/ijamr.v4i3.4317.
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<p>Solving a classical vehicle routing problem (VRP) by exact methods presents many difficulties for large dimension problem. Consequently, in multi-objective framework, heuristic or metaheuristic methods are required. Due to particular VRP structure, it seems that a dedicated heuristicis more suitable than a metaheuristic. The aim of this article is to collapse different heuristics solving classical VRP and adapt them for to solve the multi-objective vehicle routing problem (MOVRP). The so-called Cobweb Algorithm simulates spider’s behavior when weaving cobweb. This paper presents the algorithm, a didactic example, concluding remarks and way for further researches.</p>
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Mellouli, O., I. Hafidi, and A. Metrane. "A modified choice function hyper-heuristic with Boltzmann function." Mathematical Modeling and Computing 8, no. 4 (2021): 736–46. http://dx.doi.org/10.23939/mmc2021.04.736.
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Hyper-heuristics are a subclass of high-level research methods that function in a low-level heuristic research space. Their aim objective is to improve the level of generality for solving combinatorial optimization problems using two main components: a methodology for the heuristic selection and a move acceptance criterion, to ensure intensification and diversification [1]. Thus, rather than working directly on the problem's solutions and selecting one of them to proceed to the next step at each stage, hyper-heuristics operates on a low-level heuristic research space. The choice function is one of the hyper-heuristics that have proven their efficiency in solving combinatorial optimization problems [2–4]. At each iteration, the selection of heuristics is dependent on a score calculated by combining three different measures to guarantee both intensification and diversification for the heuristics choice process. The heuristic with the highest score is therefore chosen to be applied to the problem. Therefore, the key to the success of the choice function is to choose the correct weight parameters of its three measures. In this study, we make a state of the art in hyper-heuristic research and propose a new method that automatically controls these weight parameters based on the Boltzmann function. The results obtained from its application on five problem domains are compared with those of the standard, modified choice function proposed by Drake et al. [2,3].
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Felner, A., R. E. Korf, and S. Hanan. "Additive Pattern Database Heuristics." Journal of Artificial Intelligence Research 22 (November 1, 2004): 279–318. http://dx.doi.org/10.1613/jair.1480.
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We explore a method for computing admissible heuristic evaluation functions for search problems. It utilizes pattern databases, which are precomputed tables of the exact cost of solving various subproblems of an existing problem. Unlike standard pattern database heuristics, however, we partition our problems into disjoint subproblems, so that the costs of solving the different subproblems can be added together without overestimating the cost of solving the original problem. Previously, we showed how to statically partition the sliding-tile puzzles into disjoint groups of tiles to compute an admissible heuristic, using the same partition for each state and problem instance. Here we extend the method and show that it applies to other domains as well. We also present another method for additive heuristics which we call dynamically partitioned pattern databases. Here we partition the problem into disjoint subproblems for each state of the search dynamically. We discuss the pros and cons of each of these methods and apply both methods to three different problem domains: the sliding-tile puzzles, the 4-peg Towers of Hanoi problem, and finding an optimal vertex cover of a graph. We find that in some problem domains, static partitioning is most effective, while in others dynamic partitioning is a better choice. In each of these problem domains, either statically partitioned or dynamically partitioned pattern database heuristics are the best known heuristics for the problem.
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Šimon, Marek, Ladislav Huraj, Iveta Dirgová Luptáková, and Jiří Pospíchal. "Heuristics for Spreading Alarm throughout a Network." Applied Sciences 9, no. 16 (August 9, 2019): 3269. http://dx.doi.org/10.3390/app9163269.
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This paper provides heuristic methods for obtaining a burning number, which is a graph parameter measuring the speed of the spread of alarm, information, or contagion. For discrete time steps, the heuristics determine which nodes (centers, hubs, vertices, users) should be alarmed (in other words, burned) and in which order, when afterwards each alarmed node alarms its neighbors in the network at the next time step. The goal is to minimize the number of discrete time steps (i.e., time) it takes for the alarm to reach the entire network, so that all the nodes in the networks are alarmed. The burning number is the minimum number of time steps (i.e., number of centers in a time sequence alarmed “from outside”) the process must take. Since the problem is NP complete, its solution for larger networks or graphs has to use heuristics. The heuristics proposed here were tested on a wide range of networks. The complexity of the heuristics ranges in correspondence to the quality of their solution, but all the proposed methods provided a significantly better solution than the competing heuristic.
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Kappesser, J., and A. C. de C Williams. "Clinical judgement heuristics: Methods and models." European Journal of Pain 17, no. 10 (October 9, 2013): 1423–24. http://dx.doi.org/10.1002/j.1532-2149.2013.00392.x.
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Pothitos, Nikolaos, and Panagiotis Stamatopoulos. "Building Search Methods with Self-Confidence in a Constraint Programming Library." International Journal on Artificial Intelligence Tools 27, no. 04 (June 2018): 1860003. http://dx.doi.org/10.1142/s0218213018600035.
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In the late 1990s, Constrained Programming (CP) promised to separate the declaration of a problem from the process to solve it. This work attempts to serve this direction, by implementing and presenting a modular way to define search methods that seek solutions to arbitrary Constraint Satisfaction Problems (CSPs). The user just declares their CSP, and it can be solved using a portfolio of search methods already in place. Apart from the pluggable search methods framework for any CSP, we also introduce pluggable heuristics for our search methods. We found an efficient stochastic heuristics’ paradigm that smoothly combines randomness with normal heuristics. We consider a factor of disobedience to normal heuristics, and we fine-tune it each time, according to our estimation of normal heuristics’ reliability (confidence). We prove mathematically that while the disobedience factor decreases, the stochastic heuristics approximate deterministic normal heuristics. Our algebraic evidence is supported by empirical evaluations on real life problems: A new search method, namely PoPS, that exploits this heuristics’ paradigm, can outperform regular well-known constructive search methods.
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Jackovich, Petar, Bruce Cox, and Raymond R. Hill. "Comparing greedy constructive heuristic subtour elimination methods for the traveling salesman problem." Journal of Defense Analytics and Logistics 4, no. 2 (November 30, 2020): 167–82. http://dx.doi.org/10.1108/jdal-09-2020-0018.
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Purpose This paper aims to define the class of fragment constructive heuristics used to compute feasible solutions for the traveling salesman problem (TSP) into edge-greedy and vertex-greedy subclasses. As these subclasses of heuristics can create subtours, two known methodologies for subtour elimination on symmetric instances are reviewed and are expanded to cover asymmetric problem instances. This paper introduces a third novel subtour elimination methodology, the greedy tracker (GT), and compares it to both known methodologies. Design/methodology/approach Computational results for all three subtour elimination methodologies are generated across 17 symmetric instances ranging in size from 29 vertices to 5,934 vertices, as well as 9 asymmetric instances ranging in size from 17 to 443 vertices. Findings The results demonstrate the GT is the fastest method for preventing subtours for instances below 400 vertices. Additionally, a distinction between fragment constructive heuristics and the subtour elimination methodology used to ensure the feasibility of resulting solutions enables the introduction of a new vertex-greedy fragment heuristic called ordered greedy. Originality/value This research has two main contributions: first, it introduces a novel subtour elimination methodology. Second, the research introduces the concept of ordered lists which remaps the TSP into a new space with promising initial computational results.
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Khowaja, Kamran, and Dena Al-Thani. "New Checklist for the Heuristic Evaluation of mHealth Apps (HE4EH): Development and Usability Study." JMIR mHealth and uHealth 8, no. 10 (October 28, 2020): e20353. http://dx.doi.org/10.2196/20353.
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Background Diabetes is one of the leading causes of death in developing countries. Existing mobile health (mHealth) app design guidelines lack a description of the support of continuous self-monitoring of health status, behavior change to improve and adopt a healthy lifestyle, and communication with health educators and health care professionals in case of any need. Objective This paper presents the development of a specialized set of heuristics called heuristic evaluation for mHealth apps (HE4EH) as an all-in-one tool and its applicability by performing a heuristic evaluation of an mHealth app. Methods An extensive review of heuristics and checklists was used to develop the HE4EH. The HE4EH was evaluated by domain experts for heuristics, checklist items, severity ratings, and overall satisfaction. The OneTouch app, which helps individuals with diabetes manage their blood glucose levels, was evaluated using HE4EH to identify usability problems that need to be fixed in the app. Results The expert evaluation of HE4EH revealed that the heuristics were important, relevant, and clear. The checklist items across the heuristics were clear, relevant, and acceptably grouped. In terms of evaluating the OneTouch app using the HE4EH, the most frequently violated heuristics included Content, Visibility, Match, and Self-monitoring. Most of the usability problems found were minor. The system usability scale score indicated that the OneTouch app is marginally acceptable. Conclusions This heuristic evaluation using the OneTouch app shows that the HE4EH can play a vital role for designers, researchers, and practitioners to use HE4EH heuristics and checklist items as a tool to design a new or evaluate and improve an existing mHealth app.
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Murtza, Rabia, Stephen Monroe, and Robert J. Youmans. "Heuristic Evaluation for Virtual Reality Systems." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1 (September 2017): 2067–71. http://dx.doi.org/10.1177/1541931213602000.
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The advent of Virtual Reality (VR) has created new user-interaction paradigms that VR designers need to attend to in order to avoid usability issues. Currently, there are few formal methods for evaluating the usability of VR interfaces. In this paper, we introduce a new set of heuristics that can be used to carry out usability inspections of VR systems via the Heuristic Evaluation method. The heuristics were developed to identify usability problems in both developing and currently-existing VR hardware and software. The heuristics reported here were developed by surveying VR users, and then using their data to identify nine classes of usability problems common to VR systems. The result is a new resource for UXRs who are seeking to use Heuristic Review to assess VR products.
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Davidov, D., and S. Markovitch. "Multiple-Goal Heuristic Search." Journal of Artificial Intelligence Research 26 (August 25, 2006): 417–51. http://dx.doi.org/10.1613/jair.1940.
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This paper presents a new framework for anytime heuristic search where the task is to achieve as many goals as possible within the allocated resources. We show the inadequacy of traditional distance-estimation heuristics for tasks of this type and present alternative heuristics that are more appropriate for multiple-goal search. In particular, we introduce the marginal-utility heuristic, which estimates the cost and the benefit of exploring a subtree below a search node. We developed two methods for online learning of the marginal-utility heuristic. One is based on local similarity of the partial marginal utility of sibling nodes, and the other generalizes marginal-utility over the state feature space. We apply our adaptive and non-adaptive multiple-goal search algorithms to several problems, including focused crawling, and show their superiority over existing methods.
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Tarraq, Ali, Faissal Elmariami, Aziz Belfqih, and Touria Haidi. "Meta-heuristic optimization methods applied to renewable distributed generation planning: A review." E3S Web of Conferences 234 (2021): 00086. http://dx.doi.org/10.1051/e3sconf/202123400086.
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Due to its proven efficiency and computational speed, the most recent developed meta-heuristic optimization methods are widely used to better integrate renewable distributed generation (RDG) into the electricity grid. The main objective of this paper is to obtain a better knowledge of current trends in meta-heuristics applied to optimally integrate RDGs to the distribution network. This is a review of well known meta-heuristic approaches, used to solve the problem of optimal renewable distributed generation allocation planning (ORDGAP). In this context, some research gaps were mentioned, and recommendations were proposed to expand the scope of research in this field.
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Cabrera-Guerrero, Guillermo, Carolina Lagos, Carolina Castañeda, Franklin Johnson, Fernando Paredes, and Enrique Cabrera. "Parameter Tuning for Local-Search-Based Matheuristic Methods." Complexity 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/1702506.
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Algorithms that aim to solve optimisation problems by combining heuristics and mathematical programming have attracted researchers’ attention. These methods, also known as matheuristics, have been shown to perform especially well for large, complex optimisation problems that include both integer and continuous decision variables. One common strategy used by matheuristic methods to solve such optimisation problems is to divide the main optimisation problem into several subproblems. While heuristics are used to seek for promising subproblems, exact methods are used to solve them to optimality. In general, we say that both mixed integer (non)linear programming problems and combinatorial optimisation problems can be addressed using this strategy. Beside the number of parameters researchers need to adjust when using heuristic methods, additional parameters arise when using matheuristic methods. In this paper we focus on one particular parameter, which determines the size of the subproblem. We show how matheuristic performance varies as this parameter is modified. We considered a well-known NP-hard combinatorial optimisation problem, namely, the capacitated facility location problem for our experiments. Based on the obtained results, we discuss the effects of adjusting the size of subproblems that are generated when using matheuristics methods such as the one considered in this paper.
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Saab, Youssef. "New Methods for the Construction of Test Cases for Partitioning Heuristics." VLSI Design 3, no. 1 (January 1, 1995): 93–98. http://dx.doi.org/10.1155/1995/81535.
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Partitioning is an important problem in the design automation of integrated circuits. This problem in many of its formulation is NP-Hard, and several heuristic methods have been proposed for its solution. To evaluate the effectiveness of the various partitioning heuristics, it is desirable to have test cases with known optimal solutions that are as “random looking” as possible. In this paper, we describe several methods for the construction of such test cases. All our methods except one use the theory of network flow. The remaining method uses a relationship between a partitioning problem and the geometric clustering problem. The latter problem can be solved in polynomial time in any fixed dimension.
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Pedroso, João Pedro, and Mikio Kubo. "Heuristics and exact methods for number partitioning." European Journal of Operational Research 202, no. 1 (April 2010): 73–81. http://dx.doi.org/10.1016/j.ejor.2009.04.027.
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Glass, Robert L. "Formal methods vs. heuristics: Clarifying a controversy." Journal of Systems and Software 15, no. 2 (May 1991): 103–5. http://dx.doi.org/10.1016/0164-1212(91)90047-a.
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PETIT, JORDI. "COMBINING SPECTRAL SEQUENCING AND PARALLEL SIMULATED ANNEALING FOR THE MINLA PROBLEM." Parallel Processing Letters 13, no. 01 (March 2003): 77–91. http://dx.doi.org/10.1142/s0129626403001161.
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In this paper we present and analyze new sequential and parallel heuristics to approximate the Minimum Linear Arrangement problem (MinLA). The heuristics consist in obtaining a first global solution using Spectral Sequencing and improving it locally through Simulated Annealing. In order to accelerate the annealing process, we present a special neighborhood distribution that tends to favor moves with high probability to be accepted. We show how to make use of this neighborhood to parallelize the Metropolis stage on distributed memory machines by mapping partitions of the input graph to processors and performing moves concurrently. The paper reports the results obtained with this new heuristic when applied to a set of large graphs, including graphs arising from finite elements methods and graphs arising from VLSI applications. Compared to other heuristics, the measurements obtained show that the new heuristic improves the solution quality, decreases the running time and offers an excellent speedup when ran on a commodity network made of nine personal computers.
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Dimaguila, Gerardo Luis, Kathleen Gray, and Mark Merolli. "Enabling Better Use of Person-Generated Health Data in Stroke Rehabilitation Systems: Systematic Development of Design Heuristics." Journal of Medical Internet Research 22, no. 7 (July 28, 2020): e17132. http://dx.doi.org/10.2196/17132.
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Background An established and well-known method for usability assessment of various human-computer interaction technologies is called heuristic evaluation (HE). HE has been adopted for evaluations in a wide variety of specialized contexts and with objectives that go beyond usability. A set of heuristics to evaluate how health information technologies (HITs) incorporate features that enable effective patient use of person-generated health data (PGHD) is needed in an era where there is a growing demand and variety of PGHD-enabled technologies in health care and where a number of remote patient-monitoring technologies do not yet enable patient use of PGHD. Such a set of heuristics would improve the likelihood of positive effects from patients’ use of PGHD and lower the risk of negative effects. Objective This study aims to describe the development of a set of heuristics for the design and evaluation of how well remote patient therapeutic technologies enable patients to use PGHD (PGHD enablement). We used the case of Kinect-based stroke rehabilitation systems (K-SRS) in this study. Methods The development of a set of heuristics to enable better use of PGHD was primarily guided by the R3C methodology. Closer inspection of the methodology reveals that neither its development nor its application to a case study were described in detail. Thus, where relevant, each step was grounded through best practice activities in the literature and by using Nielsen’s heuristics as a basis for determining the new set of heuristics. As such, this study builds on the R3C methodology, and the implementation of a mixed process is intended to result in a robust and credible set of heuristics. Results A total of 8 new heuristics for PGHD enablement in K-SRS were created. A systematic and detailed process was applied in each step of heuristic development, which bridged the gaps described earlier. It is hoped that this would aid future developers of specialized heuristics, who could apply the detailed process of heuristic development for other domains of technology, and additionally for the case of PGHD enablement for other health conditions. The R3C methodology was also augmented through the use of qualitative studies with target users and domain experts, and it is intended to result in a robust and credible set of heuristics, before validation and refinement. Conclusions This study is the first to develop a new set of specialized heuristics to evaluate how HITs incorporate features that enable effective patient use of PGHD, with K-SRS as a key case study. In addition, it is the first to describe how the identification of initial HIT features and concepts to enable PGHD could lead to the development of a specialized set of heuristics.
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Stolz, Jörg, and Anaïd Lindemann. "The Titanic Game: Introducing Game Heuristics to Mixed Methods Theorizing and Data Analysis." Journal of Mixed Methods Research 14, no. 4 (November 12, 2019): 522–44. http://dx.doi.org/10.1177/1558689819885723.
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Despite tremendous interest in social games and game studies, the potential of game heuristics for the field of mixed methods remains unknown. This article introduces game heuristics to mixed methods research, showing how it was used in a specific study on the survival probabilities on the Titanic. Specifically, we describe how game heuristics was used to create the explanandum, code and interpret the qualitative material, and set up and interpret the quantitative model. Furthermore, we show and explicate how game heuristics was used to construct seven types of meta-inferences. The Titanic data set is especially interesting, since it is routinely used for statistical mono-method teaching; however, it can be shown that a mixed methods approach leads to a better explanation.
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Sapkal, Sagar U., and Dipak Laha. "Application of VAM to Manufacturing Scheduling Problems." Advanced Materials Research 488-489 (March 2012): 578–82. http://dx.doi.org/10.4028/www.scientific.net/amr.488-489.578.
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No-wait flow shop scheduling is a type of manufacturing scheduling which finds applications in advanced manufacturing systems and falls under the category of NP-hard combinatorial optimization problem. Heuristic methods are found to be the most suitable ones for obtaining solutions to these problems. We propose a heuristic method based on Vogel’s Approximation Method for developing the solution for the objective of minimizing total flow time in no-wait flow shop. The computational results are compared with the results of the well known existing heuristics in no-wait flow shop for minimizing the total flow time criterion. The results reveal that the proposed method significantly outperforms the existing heuristics, with comparable computational time. Statistical tests are used to validate the performance of the proposed method.
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SHIRAZI, BEHROOZ, HEE YONG YOUN, and DANIEL M. LORTS. "EVALUATION OF STATIC SCHEDULING HEURISTICS FOR REAL-TIME MULTIPROCESSING." Parallel Processing Letters 05, no. 04 (December 1995): 599–610. http://dx.doi.org/10.1142/s0129626495000539.
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Suboptimal solutions to the NP-complete problem of scheduling in a multiprocessing system are achievable with the aid of heuristic methods. Development and evaluation of static scheduling heuristics for real-time multiprocessing systems have inherited techniques from work for non-real-time systems. Unfortunately, the existing techniques compromise the results of the scheduling effort by limiting the scope of the problem. In this paper we present results of experimentation in which the parameters of the Directed Acyclic Graph (DAG) are enhanced to more correctly correspond to those of a real-world application. A method of specifying weighted combinations and priorities of simple scheduling heuristics to the scheduling algorithm is presented. Results of the compound heuristics are compared to the results of previous work in the field with some interesting conclusions.
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Eifler, Rebecca, Maximilian Fickert, Jörg Hoffmann, and Wheeler Ruml. "Refining Abstraction Heuristics during Real-Time Planning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7578–85. http://dx.doi.org/10.1609/aaai.v33i01.33017578.
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In real-time planning, the planner must select the next action within a fixed time bound. Because a complete plan may not have been found, the selected action might not lead to a goal and the agent may need to return to its current state. To preserve completeness, real-time search methods incorporate learning, in which heuristic values are updated. Previous work in real-time search has used table-based heuristics, in which the values of states are updated individually. In this paper, we explore the use of abstraction-based heuristics. By refining the abstraction on-line, we can update the values of multiple states, including ones the agent has not yet generated. We test this idea empirically using Cartesian abstractions in the Fast Downward planner. Results on various benchmarks, including the sliding tile puzzle and several IPC domains, indicate that the approach can improve performance compared to traditional heuristic updating. This work brings abstraction refinement, a powerful technique from offline planning, into the real-time setting.
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Suokas, Juho. "TESTING USABILITY METHODS IN TRANSLATION COURSES: PERSONAS AND HEURISTIC EVALUATION." CURRENT TRENDS IN TRANSLATION TEACHING AND LEARNING E 7 (December 21, 2020): 4–38. http://dx.doi.org/10.51287/cttl_e_2020_2_juho_suokas.
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To make translations better suited for specific target audiences, Suojanen et al. (2015) have suggested applying methods of user-centered translation (UCT). This study examines user-centered translation as part of university translation courses. The aim is to examine how translation students experience using two UCT methods: personas and heuristic evaluation. The students produced written comments during courses where the methods were applied. The student experience was examined by using the principles of qualitative content analysis. The analysis suggests that the methods have benefits for such matters as better understanding of the target audience and a more systematic process of evaluation. However, the methods also have drawbacks, such as the extra time and effort required. The specific heuristics were also found to be problematic to use. User-centered translation has good potential for translator training, but the specific methods require refinement. Keywords: user-centered translation, personas, heuristic evaluation, usability, translator training
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Wang, Lingya, and Dean S. Oliver. "Efficient Optimization of Well-Drilling Sequence with Learned Heuristics." SPE Journal 24, no. 05 (August 26, 2019): 2111–34. http://dx.doi.org/10.2118/195640-pa.
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Summary When preparing a field–development plan, the forecast value of the development can be sensitive to the order in which the wells are drilled. Determining the optimal drilling sequence generally requires many simulation runs. In this paper, we formulate the sequential decision problem of a drilling schedule as one of finding a path in a decision tree that is most likely to generate the highest net present value (NPV). A nonparametric online–learning methodology is developed to efficiently compute the sequence of drilling wells that is optimal or near optimal. The main ideas behind the approach are that heuristics from relaxed problems can be used to estimate the maximum value of complete drilling sequences constrained to previous wells, and that multiple online–learning techniques can be used to improve the accuracy of the estimated values. The performance of various heuristic methods is studied in a model for which uncertainty in properties is neglected. The initial heuristic used in this work generates a higher estimated NPV than the actual maximum NPV. Although such a heuristic is guaranteed to find the true optimal drilling order when used in the A* informed-search algorithm method, the cost of the search can be prohibitive unless the initial heuristic is highly accurate. For the variants of heuristic search methods with weighting parameters, the results show that it might not be possible to identify parameters that can be used to find a solution quickly without sacrificing the accuracy of the estimated NPV in this drilling–sequence problem. In contrast, the online learned heuristics derived from observations from previous drilling steps are shown to outperform the other variants of heuristic methods in terms of running time, accuracy of the estimated value, and solution quality. Multilearned heuristic search (MLHS) with space reduction (MLHS–SR) is an efficient and fast method to find a solution with high value. Continuing the search with space restoration is guaranteed to improve the solution quality or find the same solution as the MLHS without any space reduction.
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Grubenhoff, Joseph A., Sonja I. Ziniel, Lalit Bajaj, and Daniel Hyman. "Pediatric faculty knowledge and comfort discussing diagnostic errors: a pilot survey to understand barriers to an educational program." Diagnosis 6, no. 2 (June 26, 2019): 101–7. http://dx.doi.org/10.1515/dx-2018-0056.
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Abstract Background Improving Diagnosis in Healthcare calls for improved training in diagnostic reasoning and establishing non-judgmental forums to learn from diagnostic errors arising from heuristic-driven reasoning. Little is known about pediatric providers’ familiarity with heuristics or the culture surrounding forums where diagnostic errors are discussed. This study aimed to describe pediatric providers’ familiarity with common heuristics and perceptions surrounding public discussions of diagnostic errors. Methods We surveyed pediatric providers at a university-affiliated children’s hospital. The survey asked participants to identify common heuristics used during clinical reasoning (five definitions; four exemplar clinical vignettes). Participants answered questions regarding comfort publicly discussing their own diagnostic errors and barriers to sharing them. Results Seventy (30.6% response rate) faculty completed the survey. The mean number of correctly selected heuristics was 1.60/5 [standard deviation (SD)=1.13] and 1.01/4 (SD=1.06) for the definitions and vignettes, respectively. A low but significant correlation existed between correctly identifying a definition and selecting the correct heuristic in vignettes (Spearman’s ρ=0.27, p=0.02). Clinicians were significantly less likely to be “pretty” or “very” comfortable discussing diagnostic errors in public vs. private conversations (28.3% vs. 74.3%, p<0.01). The most frequently cited barriers to discussing errors were loss of reputation (62.9%) and fear of knowledge-base (58.6%) or decision-making (57.1%) being judged. Conclusions Pediatric providers demonstrated limited familiarity with common heuristics leading to diagnostic error. Greater years in practice is associated with more comfort discussing diagnostic errors, but negative peer and personal perceptions of diagnostic performance are common barriers to discussing errors publicly.
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Burke, Edmund K., Matthew R. Hyde, Graham Kendall, and John Woodward. "Automating the Packing Heuristic Design Process with Genetic Programming." Evolutionary Computation 20, no. 1 (March 2012): 63–89. http://dx.doi.org/10.1162/evco_a_00044.
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The literature shows that one-, two-, and three-dimensional bin packing and knapsack packing are difficult problems in operational research. Many techniques, including exact, heuristic, and metaheuristic approaches, have been investigated to solve these problems and it is often not clear which method to use when presented with a new instance. This paper presents an approach which is motivated by the goal of building computer systems which can design heuristic methods. The overall aim is to explore the possibilities for automating the heuristic design process. We present a genetic programming system to automatically generate a good quality heuristic for each instance. It is not necessary to change the methodology depending on the problem type (one-, two-, or three-dimensional knapsack and bin packing problems), and it therefore has a level of generality unmatched by other systems in the literature. We carry out an extensive suite of experiments and compare with the best human designed heuristics in the literature. Note that our heuristic design methodology uses the same parameters for all the experiments. The contribution of this paper is to present a more general packing methodology than those currently available, and to show that, by using this methodology, it is possible for a computer system to design heuristics which are competitive with the human designed heuristics from the literature. This represents the first packing algorithm in the literature able to claim human competitive results in such a wide variety of packing domains.
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Barrett, Clark, and Jacob Donham. "Combining SAT Methods with Non-Clausal Decision Heuristics." Electronic Notes in Theoretical Computer Science 125, no. 3 (July 2005): 3–12. http://dx.doi.org/10.1016/j.entcs.2004.09.042.
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Lam, William, Kalev Kask, Javier Larrosa, and Rina Dechter. "Residual-Guided Look-Ahead in AND/OR Search for Graphical Models." Journal of Artificial Intelligence Research 60 (October 20, 2017): 287–346. http://dx.doi.org/10.1613/jair.5475.
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We introduce the concept of local bucket error for the mini-bucket heuristics and show how it can be used to improve the power of AND/OR search for combinatorial optimization tasks in graphical models (e.g. MAP/MPE or weighted CSPs). The local bucket error illuminates how the heuristic errors are distributed in the search space, guided by the mini-bucket heuristic. We present and analyze methods for compiling the local bucket-errors (exactly and approximately) and show that they can be used to yield an effective tool for balancing look-ahead overhead during search. This can be especially instrumental when memory is restricted, accommodating the generation of only weak compiled heuristics. We illustrate the impact of the proposed schemes in an extensive empirical evaluation for both finding exact solutions and anytime suboptimal solutions.
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Lei, Yu, Maoguo Gong, Licheng Jiao, and Yi Zuo. "A memetic algorithm based on hyper-heuristics for examination timetabling problems." International Journal of Intelligent Computing and Cybernetics 8, no. 2 (June 8, 2015): 139–51. http://dx.doi.org/10.1108/ijicc-02-2015-0005.
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Purpose – The examination timetabling problem is an NP-hard problem. A large number of approaches for this problem are developed to find more appropriate search strategies. Hyper-heuristic is a kind of representative methods. In hyper-heuristic, the high-level search is executed to construct heuristic lists by traditional methods (such as Tabu search, variable neighborhoods and so on). The purpose of this paper is to apply the evolutionary strategy instead of traditional methods for high-level search to improve the capability of global search. Design/methodology/approach – This paper combines hyper-heuristic with evolutionary strategy to solve examination timetabling problems. First, four graph coloring heuristics are employed to construct heuristic lists. Within the evolutionary algorithm framework, the iterative initialization is utilized to improve the number of feasible solutions in the population; meanwhile, the crossover and mutation operators are applied to find potential heuristic lists in the heuristic space (high-level search). At last, two local search methods are combined to optimize the feasible solutions in the solution space (low-level search). Findings – Experimental results demonstrate that the proposed approach obtains competitive results and outperforms the compared approaches on some benchmark instances. Originality/value – The contribution of this paper is the development of a framework which combines evolutionary algorithm and hyper-heuristic for examination timetabling problems.
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Bisira, Hammed, and Abdellah Salhi. "Reshuffle minimisation to improve storage yard operations efficiency." Journal of Algorithms & Computational Technology 15 (January 2021): 174830262199401. http://dx.doi.org/10.1177/1748302621994010.
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There are many ways to measure the efficiency of the storage area management in container terminals. These include minimising the need for container reshuffle especially at the yard level. In this paper, we consider the container reshuffle problem for stacking and retrieving containers. The problem was represented as a binary integer programming model and solved exactly. However, the exact method was not able to return results for large instances. We therefore considered a heuristic approach. A number of heuristics were implemented and compared on static and dynamic reshuffle problems including four new heuristics introduced here. Since heuristics are known to be instance dependent, we proposed a compatibility test to evaluate how well they work when combined to solve a reshuffle problem. Computational results of our methods on realistic instances are reported to be competitive and satisfactory.
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Seipp, Jendrik, and Malte Helmert. "Counterexample-Guided Cartesian Abstraction Refinement for Classical Planning." Journal of Artificial Intelligence Research 62 (July 25, 2018): 535–77. http://dx.doi.org/10.1613/jair.1.11217.
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Counterexample-guided abstraction refinement (CEGAR) is a method for incrementally computing abstractions of transition systems. We propose a CEGAR algorithm for computing abstraction heuristics for optimal classical planning. Starting from a coarse abstraction of the planning task, we iteratively compute an optimal abstract solution, check if and why it fails for the concrete planning task and refine the abstraction so that the same failure cannot occur in future iterations. A key ingredient of our approach is a novel class of abstractions for classical planning tasks that admits efficient and very fine-grained refinement. Since a single abstraction usually cannot capture enough details of the planning task, we also introduce two methods for producing diverse sets of heuristics within this framework, one based on goal atoms, the other based on landmarks. In order to sum their heuristic estimates admissibly we introduce a new cost partitioning algorithm called saturated cost partitioning. We show that the resulting heuristics outperform other state-of-the-art abstraction heuristics in many benchmark domains.
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Knappertsbusch, Felix. "“Fractal Heuristics” for Mixed Methods Research: Applying Abbott’s “Fractal Distinctions” as a Conceptual Metaphor for Method Integration." Journal of Mixed Methods Research 14, no. 4 (December 19, 2019): 456–72. http://dx.doi.org/10.1177/1558689819893573.
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With the interest in advancing integration in the field of mixed methods research (MMR), researchers need powerful metaphors for conceiving and implementing mixing strategies. This article contributes to the field of MMR by showing how Abbott’s “fractal distinctions” provide a valuable heuristic for the application of method combinations as well as the methodological reflection of MMR. Based on its core notion, the “self-similarity” of methods approaches, I propose three heuristic guidelines: (a) deliberately seek out qualitative aspects in quantitative methods (and vice versa); (b) assume a nonessentialist, theory-oriented approach toward method integration; and (c) aim to reconcile rigor and innovation in empirical research. I discuss similarities between fractal heuristics and other conceptualizations of difference-within, including dialectical and feminist approaches to MMR.
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Golab, Amir, Ehsan Sedgh Gooya, Ayman Al Falou, and Mikael Cabon. "Review of conventional metaheuristic techniques for resource-constrained project scheduling problem." Journal of Project Management 7, no. 2 (2022): 95–110. http://dx.doi.org/10.5267/j.jpm.2021.10.002.
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This paper is concerned with an overview of the Resource-Constrained Project Scheduling Problem (RCPSP) and the conventional meta-heuristic solution techniques that have attracted the attention of many researchers in the field. Therefore, researchers have developed algorithms and methods to solve the problem. This paper addresses the single-mode RCPSP where the objective is to optimize and minimize the project duration while the quantities of resources are constrained during the project execution. In this problem, resource constraints and precedence relationships between activities are known to be the most important constraints for project scheduling. In this context, the standard RCPSP is presented. Then, the classifications of the collected papers according to the year of publication and the different meta-heuristic approaches applied are presented. Five weighted articles and their meta-heuristic techniques developed for RCPSP are described in detail and their results are summarized in the corresponding tables. In addition, researchers have developed various conventional meta-heuristic algorithms such as genetic algorithms, particle swarm optimization, ant colony optimization, bee colony optimization, simulated annealing, evolutionary algorithms, and so on. It is stated that genetic algorithms are more popular among researchers than other meta-heuristics. For this reason, the various conventional meta-heuristics and their corresponding articles are also presented to give an overview of the conventional meta-heuristic optimizing techniques. Finally, the challenges of the conventional meta-heuristics are explored, which may be helpful for future studies to apply new suitable techniques to solve the Resource-Constrained Project Scheduling Problem (RCPSP).
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Duda, J., and A. Stawowy. "Optimization Methods for Lot-Sizing Problem in an Automated Foundry." Archives of Metallurgy and Materials 58, no. 3 (September 1, 2013): 863–66. http://dx.doi.org/10.2478/amm-2013-0088.
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Abstract In the paper we studied a production planning problem in a mid-size foundry that provides tailor-made cast products in small lots for a large number of clients. Assuming that a production bottleneck is the furnace, a mixed-integer programming (MIP) model is proposed to determine the lot size of the items and the required alloys to be produced during each period of the finite planning horizon that is subdivided into smaller periods. As using an advanced commercial MIP solvers may be impractical for more complex and large problem instances, we proposed and compared a few computational intelligence heuristics i.e. tabu search, genetic algorithm and differential evolution. The examination showed that heuristic approaches can provide a good compromise between speed and quality of solutions and can be used in real-world production planning.
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Do, M., and S. Kambhampati. "SAPA: A Multi-objective Metric Temporal Planner." Journal of Artificial Intelligence Research 20 (December 1, 2003): 155–94. http://dx.doi.org/10.1613/jair.1156.
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SAPA is a domain-independent heuristic forward chaining planner that can handle durative actions, metric resource constraints, and deadline goals. It is designed to be capable of handling the multi-objective nature of metric temporal planning. Our technical contributions include (i) planning-graph based methods for deriving heuristics that are sensitive to both cost and makespan (ii) techniques for adjusting the heuristic estimates to take action interactions and metric resource limitations into account and (iii) a linear time greedy post-processing technique to improve execution flexibility of the solution plans. An implementation of SAPA using many of the techniques presented in this paper was one of the best domain independent planners for domains with metric and temporal constraints in the third International Planning Competition, held at AIPS-02. We describe the technical details of extracting the heuristics and present an empirical evaluation of the current implementation of SAPA.
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Nguyen, Thanh H., Mason Wright, Michael P. Wellman, and Satinder Singh. "Multistage Attack Graph Security Games: Heuristic Strategies, with Empirical Game-Theoretic Analysis." Security and Communication Networks 2018 (December 13, 2018): 1–28. http://dx.doi.org/10.1155/2018/2864873.
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We study the problem of allocating limited security countermeasures to protect network data from cyber-attacks, for scenarios modeled by Bayesian attack graphs. We consider multistage interactions between a network administrator and cybercriminals, formulated as a security game. This formulation is capable of representing security environments with significant dynamics and uncertainty and very large strategy spaces. We propose parameterized heuristic strategies for the attacker and defender and provide detailed analysis of their time complexity. Our heuristics exploit the topological structure of attack graphs and employ sampling methods to overcome the computational complexity in predicting opponent actions. Due to the complexity of the game, we employ a simulation-based approach and perform empirical game analysis over an enumerated set of heuristic strategies. Finally, we conduct experiments in various game settings to evaluate the performance of our heuristics in defending networks, in a manner that is robust to uncertainty about the security environment.
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SWAN, JERRY, GABRIELA OCHOA, GRAHAM KENDALL, and MARTIN EDJVET. "FITNESS LANDSCAPES AND THE ANDREWS–CURTIS CONJECTURE." International Journal of Algebra and Computation 22, no. 02 (March 2012): 1250009. http://dx.doi.org/10.1142/s0218196711006753.
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Attempts have been made to eliminate some potential counterexamples to the Andrews–Curtis conjecture using the combinatorial optimization methods of blind-search and the genetic algorithms meta-heuristic. Breadth-first search with secondary storage is currently the most successful method, which raises questions regarding the inferior performance of heuristic search. In order to understand the underlying reasons we obtain fitness landscape metrics for a number of balanced presentations and draw conclusions regarding the likely effectiveness of other meta-heuristics.
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Cetin Yagmur, Ece, and Ahmet Sarucan. "Nurse Scheduling with Opposition-Based Parallel Harmony Search Algorithm." Journal of Intelligent Systems 28, no. 4 (September 25, 2019): 633–47. http://dx.doi.org/10.1515/jisys-2017-0150.
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Abstract One of the advances made in the management of human resources for the effective implementation of service delivery is the creation of personnel schedules. In this context, especially in terms of the majority of health-care systems, creating nurse schedules comes to the fore. Nurse scheduling problem (NSP) is a complex optimization problem that allows for the preparation of an appropriate schedule for nurses and, in doing so, considers the system constraints such as legal regulations, nurses’ preferences, and hospital policies and requirements. There are many studies in the literature that use exact solution algorithms, heuristics, and meta-heuristics approaches. Especially in large-scale problems, for which deterministic methods may require too much time and cost to reach a solution, heuristics and meta-heuristic approaches come to the fore instead of exact methods. In the first phase of the study, harmony search algorithm (HSA), which has shown progress recently and can be adapted to many problems is applied for a dataset in the literature, and the algorithm’s performance is evaluated by comparing the results with other heuristics which is applied to the same dataset. As a result of the evaluation, the performance of the classical HSA is inadequate when compared to other heuristics. In the second phase of our study, by considering new approaches proposed by the literature for HSA, the effects on the algorithm’s performance of these approaches are investigated and we tried to improve the performance of the algorithm. With the results, it has been determined that the improved algorithm, which is called opposition-based parallel HSA, can be used effectively for NSPs.
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Dowding, Dawn, and Jacqueline Merrill. "The Development of Heuristics for Evaluation of Dashboard Visualizations." Applied Clinical Informatics 09, no. 03 (July 2018): 511–18. http://dx.doi.org/10.1055/s-0038-1666842.
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Background Heuristic evaluation is used in human–computer interaction studies to assess the usability of information systems. Nielsen's widely used heuristics, first developed in 1990, are appropriate for general usability but do not specifically address usability in systems that produce information visualizations. Objective This article develops a heuristic evaluation checklist that can be used to evaluate systems that produce information visualizations. Principles from Nielsen's heuristics were combined with heuristic principles developed by prior researchers specifically to evaluate information visualization. Methods We used nominal group technique to determine an appropriate final set. The combined existing usability principles and associated factors were distributed via email to a group of 12 informatics experts from a range of health care disciplines. Respondents were asked to rate each factor on its importance as an evaluation heuristic for visualization systems on a scale from 1 (definitely don't include) to 10 (definitely include). The distribution of scores for each item were calculated. A median score of ≥8 represented consensus for inclusion in the final checklist. Results Ten of 12 experts responded with rankings and written comments. The final checklist consists of 10 usability principles (7 general and 3 specific to information visualization) substantiated by 49 usability factors. Three nursing informatics experts then used the checklist to evaluate a vital sign dashboard developed for home care nurses, using a task list designed to explore the full functionality of the dashboard. The experts used the checklist without difficulty, and indicated that it covered all major usability problems encountered during task completion. Conclusion The growing capacity to generate and electronically process health data suggests that data visualization will be increasingly important. A checklist of usability heuristics for evaluating information visualization systems can contribute to assuring high quality in electronic data systems developed for health care.
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Kish, K., D. Mallery, G. Yahya Haage, R. Melgar-Melgar, M. Burke, C. Orr, N. L. Smolyar, S. Sanniti, and J. Larson. "Fostering critical pluralism with systems theory, methods, and heuristics." Ecological Economics 189 (November 2021): 107171. http://dx.doi.org/10.1016/j.ecolecon.2021.107171.
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Castro, Eduardo Meca. "Two Neighbourhood-based Approaches for the Set Covering Problem." U.Porto Journal of Engineering 5, no. 1 (February 21, 2019): 1–15. http://dx.doi.org/10.24840/2183-6493_005.001_0001.
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The Set Covering Problem is a well-known NP-complete problem which we address in this work. Due to its combinatorial nature heuristic methods, namely neighbourhood-based meta-heuristics, were used.Based on the well-known algorithms GRASP, Simulated Annealing and Variable Neighbourhood Descend, along with a constructive heuristic based on a dynamic dispatching rule to generate initial feasible solutions, two approaches to the problem were formulated. The performance of both methods was assessed in 42 instances of the problem. Our best approach has an average deviation from the best-known solution of 0.23% and reached 0% for 26 instances under 40 minutes.