Relevant bibliographies by topics / Heuristic research

Academic literature on the topic 'Heuristic research'

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Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Heuristic research"

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Hyatt, Robert P. "Heuristic Research." Tradition and Discovery: The Polanyi Society Periodical 38, no. 3 (2011): 71–74. http://dx.doi.org/10.5840/traddisc2011/201238329.

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Liang, Rui Shi, and Min Huang. "Heuristics for Domain-Independent Planning: A Survey." Applied Mechanics and Materials 135-136 (October 2011): 573–77. http://dx.doi.org/10.4028/www.scientific.net/amm.135-136.573.

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Increasing interest has been devoted to Planning as Heuristic Search over the years. Intense research has focused on deriving fast and accurate heuristics for domain-independent planning. This paper reports on an extensive survey and analysis of research work related to heuristic derivation techniques for state space search. Survey results reveal that heuristic techniques have been extensively applied in many efficient planners and result in impressive performances. We extend the survey analysis to suggest promising avenues for future research in heuristic derivation and heuristic search techniques.
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Savolainen, Reijo. "Heuristics elements of information-seeking strategies and tactics: a conceptual analysis." Journal of Documentation 73, no. 6 (October 9, 2017): 1322–42. http://dx.doi.org/10.1108/jd-11-2016-0144.

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Purpose The purpose of this paper is to elaborate the picture of strategies and tactics for information seeking and searching by focusing on the heuristic elements of such strategies and tactics. Design/methodology/approach A conceptual analysis of a sample of 31 pertinent investigations was conducted to find out how researchers have approached heuristics in the above context since the 1970s. To achieve this, the study draws on the ideas produced within the research programmes on Heuristics and Biases, and Fast and Frugal Heuristics. Findings Researchers have approached the heuristic elements in three major ways. First, these elements are defined as general level constituents of browsing strategies in particular. Second, heuristics are approached as search tips. Third, there are examples of conceptualizations of individual heuristics. Familiarity heuristic suggests that people tend to prefer sources that have worked well in similar situations in the past. Recognition heuristic draws on an all-or-none distinction of the information objects, based on cues such as information scent. Finally, representativeness heuristic is based on recalling similar instances of events or objects and judging their typicality in terms of genres, for example. Research limitations/implications As the study focuses on three heuristics only, the findings cannot be generalized to describe the use of all heuristic elements of strategies and tactics for information seeking and searching. Originality/value The study pioneers by providing an in-depth analysis of the ways in which the heuristic elements are conceptualized in the context of information seeking and searching. The findings contribute to the elaboration of the conceptual issues of information behavior research.
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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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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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Ursani, Ziauddin, and David W. Corne. "Introducing Complexity Curtailing Techniques for the Tour Construction Heuristics for the Travelling Salesperson Problem." Journal of Optimization 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/4786268.

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In this paper, complexity curtailing techniques are introduced to create faster version of insertion heuristics, that is, cheapest insertion heuristic (CIH) and largest insertion heuristic (LIH), effectively reducing their complexities fromO(n3)toO(n2)with no significant effect on quality of solution. This paper also examines relatively not very known heuristic concept of max difference and shows that it can be culminated into a full-fledged max difference insertion heuristic (MDIH) by defining its missing steps. Further to this the paper extends the complexity curtailing techniques to MDIH to create its faster version. The resultant heuristic, that is, fast max difference insertion heuristic (FMDIH), outperforms the “farthest insertion” heuristic (FIH) across a wide spectrum of popular datasets with statistical significance, even though both the heuristics have the same worst case complexity ofO(n2). It should be noted that FIH is considered best among lowest order complexity heuristics. The complexity curtailing techniques presented here open up the new area of research for their possible extension to other heuristics.
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Özcan, Ender, Mustafa Misir, Gabriela Ochoa, and Edmund K. Burke. "A Reinforcement Learning - Great-Deluge Hyper-Heuristic for Examination Timetabling." International Journal of Applied Metaheuristic Computing 1, no. 1 (January 2010): 39–59. http://dx.doi.org/10.4018/jamc.2010102603.

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Hyper-heuristics can be identified as methodologies that search the space generated by a finite set of low level heuristics for solving search problems. An iterative hyper-heuristic framework can be thought of as requiring a single candidate solution and multiple perturbation low level heuristics. An initially generated complete solution goes through two successive processes (heuristic selection and move acceptance) until a set of termination criteria is satisfied. A motivating goal of hyper-heuristic research is to create automated techniques that are applicable to a wide range of problems with different characteristics. Some previous studies show that different combinations of heuristic selection and move acceptance as hyper-heuristic components might yield different performances. This study investigates whether learning heuristic selection can improve the performance of a great deluge based hyper-heuristic using an examination timetabling problem as a case study.
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Drake, John H., Matthew Hyde, Khaled Ibrahim, and Ender Ozcan. "A genetic programming hyper-heuristic for the multidimensional knapsack problem." Kybernetes 43, no. 9/10 (November 3, 2014): 1500–1511. http://dx.doi.org/10.1108/k-09-2013-0201.

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Purpose – Hyper-heuristics are a class of high-level search techniques which operate on a search space of heuristics rather than directly on a search space of solutions. The purpose of this paper is to investigate the suitability of using genetic programming as a hyper-heuristic methodology to generate constructive heuristics to solve the multidimensional 0-1 knapsack problem Design/methodology/approach – Early hyper-heuristics focused on selecting and applying a low-level heuristic at each stage of a search. Recent trends in hyper-heuristic research have led to a number of approaches being developed to automatically generate new heuristics from a set of heuristic components. A population of heuristics to rank knapsack items are trained on a subset of test problems and then applied to unseen instances. Findings – The results over a set of standard benchmarks show that genetic programming can be used to generate constructive heuristics which yield human-competitive results. Originality/value – In this work the authors show that genetic programming is suitable as a method to generate reusable constructive heuristics for the multidimensional 0-1 knapsack problem. This is classified as a hyper-heuristic approach as it operates on a search space of heuristics rather than a search space of solutions. To our knowledge, this is the first time in the literature a GP hyper-heuristic has been used to solve the multidimensional 0-1 knapsack problem. The results suggest that using GP to evolve ranking mechanisms merits further future research effort.
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HIGGINS, A. J. "A PERCENTILE SEARCH HEURISTIC FOR GENERALIZED ASSIGNMENT PROBLEMS WITH A VERY LARGE NUMBER OF JOBS." Asia-Pacific Journal of Operational Research 22, no. 02 (June 2005): 171–88. http://dx.doi.org/10.1142/s0217595905000492.

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This article presents a new heuristic for generalized assignment problems with a very large number of jobs. The heuristic applies a probabilistic acceptance of a move, based on a percentile threshold, using information from recent moves. This percentile search heuristic (PSH) is compared to tabu search, simulated annealing, and threshold accepting using a rigorous computational experimentation with randomly generated problem instances of up to 50,000 jobs and 40 agents. The PSH did find the best solution among the heuristics for 45% of the instances, particularly larger size problems, versus 30% for tabu search, but required more fine-tuning of the heuristic parameters.
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Goldenberg, Meir. "The Heuristic Search Research Framework." Knowledge-Based Systems 129 (August 2017): 1–3. http://dx.doi.org/10.1016/j.knosys.2017.05.009.

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Dissertations / Theses on the topic "Heuristic research"

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Rolland, Erik. "Abstract heuristic search methods for graph partitioning." Connect to resource, 1991. http://rave.ohiolink.edu/etdc/view.cgi?acc%5Fnum=osu1262633923.

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AKSAKALLI, VURAL. "Heuristic Methods for Gang-Rip Saw Arbor Design and Scheduling." NCSU, 1999. http://www.lib.ncsu.edu/theses/available/etd-19991102-031914.

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AKSAKALLI, VURAL. Heuristic Methods for Gang-Rip Saw Arbor Design and Scheduling. (Under the direction of Dr. Yahya Fathi).This research considers the problem of designing and scheduling arbors for gang-rip saw systems. Such systems are typically used within the furniture manufacturing industry for processing lumber, where lumber boards are first ripped lengthwise into strips of different widths, and then, cut to the required lengths to be used in manufacturing.A saw with multiple cutting channels is used to perform this operation. This saw has fixed blades at specific positions on a rotating shaft which rips incoming lumber boards into required finished widths. The pattern of cutting channels (i.e., the setting of the blades) along the saw shaft is referred to as an ''arbor''.A typical instance of the problem consists of (1) a set of required finished widths and their corresponding demands, (2) a frequency distribution of lumber boards in the uncut stock, (3) a shaft length, and (4) a blade width. The objective is to design a set of (one or more) arbors and the corresponding quantity of lumber to run through each arbor, such that the total amount of waste generated is minimized while the demand is satisfied.In the research, we focus on solving the problem using only one arbor. First, we discuss the computational complexity of the problem and propose a total enumeration procedure which can be used to solve relatively small instances. Then, we develop algorithms based on heuristic approaches such as local improvement procedures, simulated annealing, and genetic algorithms. Our computational experiments indicate that a local improvement procedure with two nested loops, performing local search with a different neighborhood structure within each loop, gives very high quality solutions to the problem within very short execution times.

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Höck, Barbar Katja. "An examination of heuristic algorithms for the travelling salesman problem." Master's thesis, University of Cape Town, 1988. http://hdl.handle.net/11427/22268.

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The role of heuristics in combinatorial optimization is discussed. Published heuristics for the Travelling Salesman Problem (TSP) were reviewed and morphological boxes were used to develop new heuristics for the TSP. New and published heuristics were programmed for symmetric TSPs where the triangle inequality holds, and were tested on micro computer. The best of the quickest heuristics was the furthest insertion heuristic, finding tours 3 to 9% above the best known solutions (2 minutes for 100 nodes). Better results were found by longer running heuristics, e.g. the cheapest angle heuristic (CCAO), 0-6% above best (80 minutes for 100 nodes). The savings heuristic found the best results overall, but took more than 2 hours to complete. Of the new heuristics, the MST path algorithm at times improved on the results of the furthest insertion heuristic while taking the same time as the CCAO. The study indicated that there is little likelihood of improving on present methods unless a fundamental new approach is discovered. Finally a case study using TSP heuristics to aid the planning of grid surveys was described.
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Maye, P. J. "Exact and heuristic methods of finding network structure in linear programs." Thesis, Coventry University, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.276165.

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Asgharzadeh, Talebi Zohreh. "Exact and Heuristic Methods for solving the View-Selection Problem for Aggregate Queries." NCSU, 2006. http://www.lib.ncsu.edu/theses/available/etd-05092006-123513/.

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In this thesis we present a formal study of the following view-selection problem: Given a set of queries, a database, and an upper bound on the amount of disk space that can be used to store materialized views, return definitions of views that, when materialized in the database, would reduce the evaluation costs of the queries. Optimizing the layout of stored data using view selection has a direct impact on the performance of the entire database system. At the same time, the optimization problem is intractable, even under natural restrictions on the types of queries of interest. We introduce an integer-programming model to obtain optimal solutions for the view-selection problem for aggregate queries on data warehouses. Through a computational experiment we show that this model can be used to solve realistic-size instances of the problem. We also report the results of the post-optimality analysis that we performed to determine the impact of changing certain input characteristics on the optimal solution. We solve large instances by applying several methods of reducing the size of the search space. We compare our approach to the leading heuristic procedure in the field [20].
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Samson, Duncan Alistair. "The heuristic significance of enacted visualisation." Thesis, Rhodes University, 2012. http://hdl.handle.net/10962/d1003434.

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This study is centred on an analysis of pupils' lived experience while engaged in the generalisation of linear sequences/progressions presented in a pictorial context. The study is oriented within the conceptual framework of qualitative research, and is anchored within an interpretive paradigm. A case study methodological strategy was adopted, the research participants being the members of a mixed gender, high ability Grade 9 class of 23 pupils at an independent school in South Africa. The analytical framework is structured around a combination of complementary multiple perspectives provided by three theoretical ideas, enactivism, figural apprehension, and knowledge objectification. An important aspect of this analytical framework is the sensitivity it shows to the visual, phenomenological and semiotic aspects of figural pattern generalisation. It is the central thesis of this study that the combined complementary multiple perspectives of enactivism, figural apprehension and knowledge objectification provide a powerful depth of analysis to the exploration of the inter-relationship between the embodied processes of pattern generalisation and the visualisation of pictorial cues. The richly textured tapestry of activity captured through a multi-systemic semiotic analysis of participants' generalisation activity stands testament to this central thesis. Insights gleaned from this study are presented as practical strategies which support and encourage a multiple representational approach to pattern generalisation in the pedagogical context of the classroom.
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Khambhampati, Surya Sudha. "A Tabu Search Heuristic for Multi-Period Clustering to Rationalize Delivery Operations." Wright State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=wright1210959864.

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Shah, Mahesh. "A heuristic method for the optimal design of water distribution systems." Thesis, Virginia Polytechnic Institute and State University, 1987. http://hdl.handle.net/10919/88600.

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The water distribution system design problem consists of finding a minimum cost combination of network layout and sizes of system components so as to satisfy flow demands, minimum and maximum head requirements and a reliability criterion. A two step procedure is proposed to find a near optimal design. The first step considers obtaining a near optimal tree layout using a heuristic tree search algorithm. Two different methods are followed for the tree search - one for single source networks and the other for multiple source networks. The second step adds loop forming redundant links to the tree layout in such a way that every demand node has two paths from source node(s). The methodology is applied to a single source network and a multiple source network. In both the cases better results arc achieved than those obtained previously by other researchers.
Master of Science
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Cain, Mark J. "A GAMS-based model of the U.S. Army Wartime Ammunition Distribution System for the Corps level." Thesis, Monterey, California. Naval Postgraduate School, 1985. http://hdl.handle.net/10945/23244.

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Approved for public release; distribution is unlimited
The U.S. Army Wartime Ammunition Distribution System (WADS) will experience an unprecedented demand for ammunition under the operational concept of Airland Battle. To meet demand, proper storage facility location and an efficient flow through the distribution network will be required. Using information from Army Field Manuals, maps and simulation data for demand, both a mixed integer program (MIP) and a sequential, optimization-based heuristic are developed to model the WADS. The Generalized Algebraic Modelling System is used to implement both models. The sequential heuristic locates ammunition facilities with a binary integer program and then directs ammunition through those facilities utilizing a network flow model with side constraints. The MIP integrates location and flow decisions in the same model. For a general scenario, the sequential heuristic locates a 21 node, 30 arc network with ammunition flows over 30 time periods in 22 CPU seconds on an IBM 3033AP. For the same scenario the MIP obtains a solution for only a 3 time period problem in 87 CPU seconds. Keywords: Ammunition, Integer programming, Heuristic, Networks
http://archive.org/details/gamsbasedmodelof00cain
Captain, United States Army
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Hiremath, Chaitr. "New Heuristic And Metaheuristic Approaches Applied To The Multiple-choice Multidimensional Knapsack Problem." Wright State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=wright1203960454.

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Books on the topic "Heuristic research"

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Moustakas, Clark E. Heuristic research: Design, methodology, and applications. Newbury Park: Sage Publications, 1990.

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Austin, Veronica. Learning from experience: Heuristic research study based on one music therapy session. [Guildford]: [University of Surrey], 1999.

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Pietraś, Ziemowit Jacek. Sztuczna inteligencja w politologii: Heurystyczne modelowanie procesów adaptacji politycznej. Lublin: Wydawn. Uniwersytetu Marii Curie-Skłodowskiej, 1990.

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Linear A and Cypro-Minoan in the light of heuristics and cryptology: A research report. Szczecin: B.Z. Szałek, 2005.

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Woodruff, David L. Advances in Computational and Stochastic Optimization, Logic Programming, and Heuristic Search: Interfaces in Computer Science and Operations Research. Boston, MA: Springer US, 1998.

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Farahutdinov, Shamil'. Current trends and innovative methods in marketing research. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1016648.

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This tutorial provides an overview of modern and innovative research methods used in marketing. The main focus is on innovative methods that are just becoming widespread, as well as on traditional methods that are being transformed as a result of existing trends in the modern digital age. The theoretical and technical aspects underlying the methods under consideration, a brief history of their origin, heuristic possibilities and limitations are revealed. In some cases, examples of use are provided, as well as indications of useful resources and practical use of methods in individual research practice. Meets the requirements of the Federal state educational standards of higher education of the latest generation. It is the basis for studying the discipline "Modern methods of sociological research", and its separate sections can complement such disciplines as "Methodology and methods of sociological research", "methods of marketing research", "data Analysis in sociology", etc. The materials of the manual can also be useful for independent researchers, business representatives, and managers. For students and postgraduates studying in groups of specialties and training areas 38.00.00 "Economics and management" and 39.00.00 "Sociology and social work".
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Simulating science: Heuristics, mental models, and technoscientific thinking. Bloomington: Indiana University Press, 1992.

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Szałek, Benon Zbigniew. The etruscan problem in the light of heuristics: A research report. Shczecin: B.Z. Szałek, 2003.

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Szałek, Benon Zbigniew. The mayan problem in the light of heuristics: (a research report). Szczecin [Poland]: PPH ZAPOL, 2004.

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Vo€, Stefan. Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization. Boston, MA: Springer US, 1999.

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Book chapters on the topic "Heuristic research"

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Salhi, Saïd. "Applications, Conclusion and Research Challenges." In Heuristic Search, 189–209. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49355-8_7.

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Jaiswal, N. K. "Heuristic Optimization." In Military Operations Research, 169–208. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6275-7_7.

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Gass, Saul I., and Carl M. Harris. "heuristic." In Encyclopedia of Operations Research and Management Science, 731. New York, NY: Springer US, 2001. http://dx.doi.org/10.1007/1-4020-0611-x_905.

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Gass, Saul I., and Carl M. Harris. "heuristic." In Encyclopedia of Operations Research and Management Science, 731. New York, NY: Springer US, 2001. http://dx.doi.org/10.1007/1-4020-0611-x_906.

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Klein, Robert. "Heuristic Procedures." In Operations Research/Computer Science Interfaces Series, 161–211. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4629-0_5.

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Salomon, Marc, Roelof Kuik, and Luk N. Van Wassenhove. "A Lagrangian Heuristic for Multilevel Lotsizing." In Operations Research Proceedings, 433–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77254-2_49.

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Lang, Rainer, and Thomas Milius. "A Heuristic for the Nesting Problem." In Operations Research ’92, 130–31. Heidelberg: Physica-Verlag HD, 1993. http://dx.doi.org/10.1007/978-3-662-12629-5_36.

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Höhn, Sviatlana, and Kerstin Bongard-Blanchy. "Heuristic Evaluation of COVID-19 Chatbots." In Chatbot Research and Design, 131–44. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68288-0_9.

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Dresbach, Stefan. "A New Heuristic Layout Algorithm for DAGs." In Operations Research Proceedings, 121–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-79459-9_23.

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Burke, Edmund, Moshe Dror, Sanja Petrovic, and Rong Qu. "Hybrid Graph Heuristics within a Hyper-Heuristic Approach to Exam Timetabling Problems." In Operations Research/Computer Science Interfaces Series, 79–91. Boston, MA: Springer US, 2005. http://dx.doi.org/10.1007/0-387-23529-9_6.

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Conference papers on the topic "Heuristic research"

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Downs, Simon T., and Claire A. Lerpiniere. "The Future of Heuristic Fossils." In Design Research Society Conference 2018. Design Research Society, 2018. http://dx.doi.org/10.21606/drs.2018.612.

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Zhou, Qiang, Yanbin Fan, and Changjiang Wei. "Heuristic Routing Protocol Research on Opportunistic Networks." In 2012 IEEE 14th Int'l Conf. on High Performance Computing and Communication (HPCC) & 2012 IEEE 9th Int'l Conf. on Embedded Software and Systems (ICESS). IEEE, 2012. http://dx.doi.org/10.1109/hpcc.2012.254.

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Li, Fa-chao, and Fei Guan. "Heuristic Model Research on Decision Tree Algorithm." In 2009 International Asia Symposium on Intelligent Interaction and Affective Computing (ASIA). IEEE, 2009. http://dx.doi.org/10.1109/asia.2009.34.

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Liu, Yang, and Miao Zhang. "Financial websites oriented heuristic anti-phishing research." In 2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems (CCIS). IEEE, 2012. http://dx.doi.org/10.1109/ccis.2012.6664247.

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Dou, Jihua, Xingbao Yang, and Chuanwei Zhu. "Heuristic Teaching Method Research for Complex Systems." In Proceedings of the 5th International Conference on Arts, Design and Contemporary Education (ICADCE 2019). Paris, France: Atlantis Press, 2019. http://dx.doi.org/10.2991/icadce-19.2019.162.

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Piyadasun, Thilina, Buwaneka Kalansuriya, Malaka Gangananda, Minudika Malshan, H. M. N. Dilum Bandara, and Suresh Marru. "Rationalizing police patrol beats using heuristic-based clustering." In 2017 Moratuwa Engineering Research Conference (MERCon). IEEE, 2017. http://dx.doi.org/10.1109/mercon.2017.7980523.

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Steinmetz, Marcel, and Joerg Hoffmann. "LP Heuristics over Conjunctions: Compilation, Convergence, Nogood Learning." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/672.

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Two strands of research in classical planning are LP heuristics and conjunctions to improve approximations. Combinations of the two have also been explored. Here, we focus on convergence properties, forcing the LP heuristic to equal the perfect heuristic h* in the limit. We show that, under reasonable assumptions, partial variable merges are strictly dominated by the compilation Pi^C of explicit conjunctions, and that both render the state equation heuristic equal to h* for a suitable set C of conjunctions. We show that consistent potential heuristics can be computed from a variant of Pi^C, and that such heuristics can represent h* for suitable C. As an application of these convergence properties, we consider sound nogood learning in state space search, via refining the set C. We design a suitable refinement method to this end. Experiments on IPC benchmarks show significant performance improvements in several domains.
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Liu-bin, SHANG, Wang Wei, and Liu Zhi-hua. "Research on Heuristic Mutated Particle Swarm Optimization Algorithm." In 2019 IEEE International Conference on Unmanned Systems (ICUS). IEEE, 2019. http://dx.doi.org/10.1109/icus48101.2019.8996019.

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Li, Yang, Yan Sun, Chengshan Han, Xiaodong Wang, and Shuyan Xu. "Algorithm Research of Flexible Graphplan based on Heuristic." In 2008 9th International Conference for Young Computer Scientists (ICYCS). IEEE, 2008. http://dx.doi.org/10.1109/icycs.2008.473.

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Bianchini, Carlo, Alessandro Viscogliosi, and Alessandro Aglietti. "Innovative Digital Heuristic Approaches in Architectural Historical Research." In 2017 21st International Conference on Information Visualisation (IV). IEEE, 2017. http://dx.doi.org/10.1109/iv.2017.47.

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