Relevant bibliographies by topics / Heuristics

Academic literature on the topic 'Heuristics'

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Published: 4 June 2021
Last updated: 22 June 2024

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Journal articles on the topic "Heuristics"

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Seipp, Jendrik. "Better Orders for Saturated Cost Partitioning in Optimal Classical Planning." Proceedings of the International Symposium on Combinatorial Search 8, no. 1 (September 1, 2021): 149–53. http://dx.doi.org/10.1609/socs.v8i1.18438.

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Cost partitioning is a general method for adding multiple heuristic values admissibly. In the setting of optimal classical planning, saturated cost partitioning has recently been shown to be the cost partitioning algorithm of choice for pattern database heuristics found by hill climbing, systematic pattern database heuristics and Cartesian abstraction heuristics. To evaluate the synergy of the three heuristic types, we compute the saturated cost partitioning over the combined sets of heuristics and observe that the resulting heuristic is outperformed by the heuristic that simply maximizes over the three saturated cost partitioning heuristics computed separately for each heuristic type. Our new algorithm for choosing the orders in which saturated cost partitioning considers the heuristics allows us to compute heuristics outperforming not only the maximizing heuristic but even state-of-the-art planners.
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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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Sanggala, Ekra, and Muhammad Ardhya Bisma. "Perbandingan Savings Algorithm dengan Nearest Neighbour dalam Menyelesaikan Russian TSP Instances." Jurnal Media Teknik dan Sistem Industri 7, no. 1 (March 31, 2023): 27. http://dx.doi.org/10.35194/jmtsi.v7i1.3039.

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Travelling Salesman Problem (TSP) is the problem for finding the shortest route starting from start node then visiting number of nodes exactly once and finally go back to start node. Several heuristics are popular for solving TSP, for example Savings Algorithm and Nearest Neighbour. Performance heuristics on solving TSP are diverse, so there is need of reference for choosing a heuristic. Comparing heuristics on solving instance can be a reference for choosing a heuristic. This paper will discuss about comparison Savings Algorithm and Nearest Neighbour on Solving Russian TSP Instances. For generating length of route, Savings Algorithm is better than Nearest Neighbour, while for generating CPU time, Nearest Neighbour is better than Savings Algorithm. Travelling Salesman Problem (TSP) merupakan permasalahan penentuan rute terpendek yang diawali dari titik start untuk mengunjungi sekumpulan titik tepat sekali dan diakhiri dengan kembali ke titik start. Beberapa Heuristik yang cukup populer untuk menyelesaikan TSP antara lain Savings Algorithm dan Nearest Neighbour. Kemampuan Heuristik dalam menyelesaikan TSP berbeda-beda, sehingga diperlukan sebuah acuan untuk menentukan Heuristik yang akan digunakan. Membandingkan Heuristik dalam menyelesaikan instance dapat menjadi acuan untuk pemilihan Heuristik. Pada paper ini akan dibahas mengenai perbandingan Savings Algorithm dan Nearest Neighbour dalam menyelesaikan Russian TSP Instances. Untuk panjang rute yang dihasilkan, maka Savings Algorithm lebih baik dibandingkan Nearest Neighbour, sedangkan untuk CPU Time yang dihasilkan, maka Nearest Neighbour lebih baik dibandingkan Savings Algorithm.
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Ursani, Ziauddin, and Ahsan Ahmad Ursani. "Augmented tour construction heuristics for the travelling salesman problem." International Journal of Industrial Optimization 4, no. 2 (September 11, 2023): 131–44. http://dx.doi.org/10.12928/ijio.v4i2.7875.

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Tour construction heuristics serve as fundamental techniques in optimizing the routes of a traveling salesman. These heuristics remain significant as foundational methods for generating initial solutions to the Traveling Salesman Problem (TSP), facilitating subsequent applications of tour improvement heuristics. These heuristics effectively comprise the iterative application of city node selection and insertion. However, thus far, no attempts have been made to enhance the basic structure of tour construction heuristics to bring a better initial solution for the advanced heuristics. This study aims to enhance tour construction heuristics without compromising their theoretical complexity. Specifically, an iterative step of partial tour deconstruction has been introduced to the existing heuristics. This additional step has been implemented and evaluated with three highly performing tour construction heuristics: the farthest insertion heuristic, the max difference insertion heuristic, and the fast max difference insertion heuristic. The results demonstrate that augmenting these heuristics with the partial tour deconstruction step improves the best, worst, and average solutions while preserving their theoretical complexity
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Shperberg, Shahaf, Ariel Felner, Lior Siag, and Nathan R. Sturtevant. "On the Properties of All-Pair Heuristics." Proceedings of the International Symposium on Combinatorial Search 17 (June 1, 2024): 127–33. http://dx.doi.org/10.1609/socs.v17i1.31550.

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While most work in heuristic search concentrates on goal-specific heuristics, which estimate the shortest path cost from any state to the goal, we explore all-pair heuristics that estimate distances between all pairs of states. We examine the relationship between these heuristic functions and the shortest distance function they estimate, revealing that all-pair consistent heuristics may violate the triangle inequality. Thus, we introduce a new property for heuristics called Δ-consistency, requiring adherence to the triangle inequality. Additionally, we present a method for transforming standard consistent heuristics to be Δ-consistent, showcasing its benefits through a synthetic example. We then show that common heuristic families inherently exhibit Δ-consistency. This positive finding encourages the use of all-pair consistent heuristics, and prompts further investigation into the optimality of A*, when given an all-pair heuristic instead of a goal-specific heuristic.
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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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Seipp, Jendrik, Florian Pommerening, and Malte Helmert. "New Optimization Functions for Potential Heuristics." Proceedings of the International Conference on Automated Planning and Scheduling 25 (April 8, 2015): 193–201. http://dx.doi.org/10.1609/icaps.v25i1.13714.

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Potential heuristics, recently introduced by Pommerening et al., characterize admissible and consistent heuristics for classical planning as a set of declarative constraints. Every feasible solution for these constraints defines an admissible heuristic, and we can obtain heuristics that optimize certain criteria such as informativeness by specifying suitable objective functions. The original paper only considered one such objective function: maximizing the heuristic value of the initial state. In this paper, we explore objectives that attempt to maximize heuristic estimates for all states (reachable and unreachable), maximize heuristic estimates for a sample of reachable states, maximize the number of detected dead ends, or minimize search effort. We also search for multiple heuristics with complementary strengths that can be combined to obtain even better heuristics.
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Narayanan, Venkatraman, Sandip Aine, and Maxim Likhachev. "Improved Multi-Heuristic A* for Searching with Uncalibrated Heuristics." Proceedings of the International Symposium on Combinatorial Search 6, no. 1 (September 1, 2021): 78–86. http://dx.doi.org/10.1609/socs.v6i1.18350.

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Recently, several researchers have brought forth the benefits of searching with multiple (and possibly inadmissible) heuristics, arguing how different heuristics could be independently useful in different parts of the state space. However, algorithms that use inadmissible heuristics in the traditional best-first sense, such as the recently developed Multi-Heuristic A* (MHA*), are subject to a crippling calibration problem: they prioritize nodes for expansion by additively combining the cost-to-come and the inadmissible heuristics even if those heuristics have no connection with the cost-to-go (e.g., the heuristics are uncalibrated) . For instance, if the inadmissible heuristic were an order of magnitude greater than the perfect heuristic, an algorithm like MHA* would simply reduce to a weighted A* search with one consistent heuristic. In this work, we introduce a general multi-heuristic search framework that solves the calibration problem and as a result a) facilitates the effective use of multiple uncalibrated inadmissible heuristics, and b) provides significantly better performance than MHA* whenever tighter sub-optimality bounds on solution quality are desired. Experimental evaluations on a complex full-body robotics motion planning problem and large sliding tile puzzles demonstrate the benefits of our framework.
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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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Pommerening, Florian, Gabriele Röger, Malte Helmert, and Blai Bonet. "LP-Based Heuristics for Cost-Optimal Planning." Proceedings of the International Conference on Automated Planning and Scheduling 24 (May 11, 2014): 226–34. http://dx.doi.org/10.1609/icaps.v24i1.13621.

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Many heuristics for cost-optimal planning are based on linear programming. We cover several interesting heuristics of this type by a common framework that fixes the objective function of the linear program. Within the framework, constraints from different heuristics can be combined in one heuristic estimate which dominates the maximum of the component heuristics. Different heuristics of the framework can be compared on the basis of their constraints. With this new method of analysis, we show dominance of the recent LP-based state-equation heuristic over optimal cost partitioning on single-variable abstractions. We also show that the previously suggested extension of the state-equation heuristic to exploit safe variables cannot lead to an improved heuristic estimate. We experimentally evaluate the potential of the proposed framework on an extensive suite of benchmark tasks.
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Dissertations / Theses on the topic "Heuristics"

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Peake, Katharine Louise. "Composition heuristics and theories and a proposed heuristic for business writing." CSUSB ScholarWorks, 2007. https://scholarworks.lib.csusb.edu/etd-project/3282.

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Monk, Monika Patrice. "Mobile Exergaming Heuristics." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/50581.

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An alarming number of adolescents experience obesity and related health issues, in part because of a lack of exercise. Increased mobile technology availability can have negative effects on amount of exercise, but they can have positive effects as well. Leveraging mobile technology to encourage and motivate exercise has potential to decrease unhealthy lifestyles, especially among young people. Mobile exergaming is an emerging field that has the potential to motivate users to exercise while also having fun. However, much of the early development work on mobile exergames has been ad-hoc, with little guidance available for designers. This work seeks to identify heuristics catered for mobile exergaming. This thesis presents four mobile exergaming heuristics were identified based on recent literature and on the author's mobile exergame design and development efforts: 1) Motivational game concepts that promote physical activity; 2) Game cues that engage active users; 3) Physically and temporally appropriate game structure to encourage continual, recurring play; 4) Game play movements that are safe for the user and for the device. This thesis describes the development of the mobile exergame heuristics, along with the creation and distribution of an ExergameApp Suite comprised of three mobile exergames: Fish Out of Water, Color Hunt and Space Rayders.
Master of Science
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Yee, Michael 1978. "Inferring noncompensatory choice heuristics." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/36226.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2006.
Includes bibliographical references (p. 121-128).
Human decision making is a topic of great interest to marketers, psychologists, economists, and others. People are often modeled as rational utility maximizers with unlimited mental resources. However, due to the structure of the environment as well as cognitive limitations, people frequently use simplifying heuristics for making quick yet accurate decisions. In this research, we apply discrete optimization to infer from observed data if a person is behaving in way consistent with a choice heuristic (e.g., a noncompensatory lexicographic decision rule). We analyze the computational complexity of several inference related problems, showing that while some are easy due to possessing a greedoid language structure, many are hard and likely do not have polynomial time solutions. For the hard problems we develop an exact dynamic programming algorithm that is robust and scalable in practice, as well as analyze several local search heuristics. We conduct an empirical study of SmartPhone preferences and find that the behavior of many respondents can be explained by lexicographic strategies.
(cont.) Furthermore, we find that lexicographic decision rules predict better on holdout data than some standard compensatory models. Finally, we look at a more general form of noncompensatory decision process in the context of consideration set formation. Specifically, we analyze the computational complexity of rule-based consideration set formation, develop solution techniques for inferring rules given observed consideration data, and apply the techniques to a real dataset.
by Michael J. Yee.
Ph.D.
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Panthulu, Pradeep. "Intelligent Memory Management Heuristics." Thesis, University of North Texas, 2003. https://digital.library.unt.edu/ark:/67531/metadc4399/.

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Automatic memory management is crucial in implementation of runtime systems even though it induces a significant computational overhead. In this thesis I explore the use of statistical properties of the directed graph describing the set of live data to decide between garbage collection and heap expansion in a memory management algorithm combining the dynamic array represented heaps with a mark and sweep garbage collector to enhance its performance. The sampling method predicting the density and the distribution of useful data is implemented as a partial marking algorithm. The algorithm randomly marks the nodes of the directed graph representing the live data at different depths with a variable probability factor p. Using the information gathered by the partial marking algorithm in the current step and the knowledge gathered in the previous iterations, the proposed empirical formula predicts with reasonable accuracy the density of live nodes on the heap, to decide between garbage collection and heap expansion. The resulting heuristics are tested empirically and shown to improve overall execution performance significantly in the context of the Jinni Prolog compiler's runtime system.
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Petracca, Enrico <1983&gt. "Essays in structural heuristics." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amsdottorato.unibo.it/6284/1/Petracca_Enrico_Tesi.pdf.

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This dissertation introduces and develops a new method of rational reconstruction called structural heuristics. Structural heuristics takes assignment of structure to any given object of investigation as the starting point for its rational reconstruction. This means to look at any given object as a system of relations and of transformation laws for those relations. The operational content of this heuristics can be summarized as follows: when facing any given system the best way to approach it is to explicitly look for a possible structure of it. The utilization of structural heuristics allows structural awareness, which is considered a fundamental epistemic disposition, as well as a fundamental condition for the rational reconstruction of systems of knowledge. In this dissertation, structural heuristics is applied to reconstructing the domain of economic knowledge. This is done by exploring four distinct areas of economic research: (i) economic axiomatics; (ii) realism in economics; (iii) production theory; (iv) economic psychology. The application of structural heuristics to these fields of economic inquiry shows the flexibility and potential of structural heuristics as epistemic tool for theoretical exploration and reconstruction.
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Petracca, Enrico <1983&gt. "Essays in structural heuristics." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amsdottorato.unibo.it/6284/.

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This dissertation introduces and develops a new method of rational reconstruction called structural heuristics. Structural heuristics takes assignment of structure to any given object of investigation as the starting point for its rational reconstruction. This means to look at any given object as a system of relations and of transformation laws for those relations. The operational content of this heuristics can be summarized as follows: when facing any given system the best way to approach it is to explicitly look for a possible structure of it. The utilization of structural heuristics allows structural awareness, which is considered a fundamental epistemic disposition, as well as a fundamental condition for the rational reconstruction of systems of knowledge. In this dissertation, structural heuristics is applied to reconstructing the domain of economic knowledge. This is done by exploring four distinct areas of economic research: (i) economic axiomatics; (ii) realism in economics; (iii) production theory; (iv) economic psychology. The application of structural heuristics to these fields of economic inquiry shows the flexibility and potential of structural heuristics as epistemic tool for theoretical exploration and reconstruction.
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Clark, Andrew J. "Optimisation heuristics for cryptology." Thesis, Queensland University of Technology, 1998. https://eprints.qut.edu.au/15777/1/Andrew_Clark_Thesis.pdf.

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The aim of the research presented in this thesis is to investigate the use of various optimisation heuristics in the fields of automated cryptanalysis and automated cryptographic function generation. These techniques were found to provide a successful method of automated cryptanalysis of a variety of the classical ciphers. Also, they were found to enhance existing fast correlation attacks on certain stream ciphers. A previously proposed attack of the knapsack cipher is shown to be flawed due to the absence of a suitable solution evaluation mechanism. Finally, a new approach for finding highly nonlinear Boolean functions is introduced.
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Clark, Andrew J. "Optimisation Heuristics for Cryptology." Queensland University of Technology, 1998. http://eprints.qut.edu.au/15777/.

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The aim of the research presented in this thesis is to investigate the use of various optimisation heuristics in the fields of automated cryptanalysis and automated cryptographic function generation. These techniques were found to provide a successful method of automated cryptanalysis of a variety of the classical ciphers. Also, they were found to enhance existing fast correlation attacks on certain stream ciphers. A previously proposed attack of the knapsack cipher is shown to be flawed due to the absence of a suitable solution evaluation mechanism. Finally, a new approach for finding highly nonlinear Boolean functions is introduced.
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Lü, Haili, and 吕海利. "A comparative study of assembly job shop scheduling using simulation, heuristics and meta-heuristics." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B47029018.

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Pasch, Kenneth Alan. "Heuristics for Job-Shop Scheduling." Thesis, Massachusetts Institute of Technology, 1988. http://hdl.handle.net/1721.1/6847.

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Two methods of obtaining approximate solutions to the classic General Job-shop Scheduling Program are investigated. The first method is iterative. A sampling of the solution space is used to decide which of a collection of space pruning constraints are consistent with "good" schedules. The selected space pruning constraints are then used to reduce the search space and the sampling is repeated. This approach can be used either to verify whether some set of space pruning constraints can prune with discrimination or to generate solutions directly. Schedules can be represented as trajectories through a Cartesian space. Under the objective criteria of Minimum maximum Lateness family of "good" schedules (trajectories) are geometric neighbors (reside with some "tube") in this space. This second method of generating solutions takes advantage of this adjacency by pruning the space from the outside in thus converging gradually upon this "tube." One the average this methods significantly outperforms an array of the Priority Dispatch rules when the object criteria is that of Minimum Maximum Lateness. It also compares favorably with a recent relaxation procedure.
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Books on the topic "Heuristics"

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Osman, Ibrahim H., and James P. Kelly, eds. Meta-Heuristics. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1361-8.

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B, Fogel David, ed. How to solve it: Modern heuristics. 2nd ed. Berlin: Springer, 2004.

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Andre, Carlos, Reis Pinheiro, and Fiona McNeill, eds. Heuristics in Analytics. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118434260.

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Martí, Rafael, Pardalos Panos, and Mauricio G. C. Resende, eds. Handbook of Heuristics. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-07153-4.

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The heuristics debate. Oxford: Oxford University Press, 2011.

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B, Fogel David, ed. How to solve it: Modern heuristics. Berlin: Springer, 2000.

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Gigerenzer, Gerd. Simple heuristics that make us smart. New York: Oxford University Press, 1999.

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Object-oriented design heuristics. Reading, Mass: Addison-Wesley Pub. Co., 1996.

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Hartman, Jan. Heurystyka filozoficzna: Philosophical heuristics. Toruń: Wydawnictwo Naukowe Uniwersytetu Mikołaja Kopernika, 2011.

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Chatfield, Tom, and Tom Chatfield. Heuristics and Cognitive Biases. 2455 Teller Road, Thousand Oaks California 91320: SAGE Publications, Inc., 2022. http://dx.doi.org/10.4135/9781071880029.

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Book chapters on the topic "Heuristics"

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Pirrone, Angelo, Peter C. R. Lane, Laura Bartlett, Noman Javed, and Fernand Gobet. "Heuristic Search of Heuristics." In Artificial Intelligence XL, 407–20. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-47994-6_36.

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Bard, Jonathan F. "Heuristics." In Nonconvex Optimization and Its Applications, 361–88. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-2836-1_9.

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Meyn, Sean P., and Richard L. Tweedie. "Heuristics." In Markov Chains and Stochastic Stability, 3–22. London: Springer London, 1993. http://dx.doi.org/10.1007/978-1-4471-3267-7_1.

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Fortz, Bernard. "Heuristics." In Network Theory and Applications, 115–23. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4669-6_7.

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Laguna, Manuel, and Rafael Martí. "Heuristics." In Encyclopedia of Operations Research and Management Science, 695–703. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4419-1153-7_1184.

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Gonzales, Josh, and Sandeep Mishra. "Heuristics." In Encyclopedia of Evolutionary Psychological Science, 1–3. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-16999-6_626-1.

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Rubaltelli, Enrico. "Heuristics." In The Palgrave Encyclopedia of the Possible, 1–9. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-98390-5_1-1.

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Li, PhD, Haksun. "Heuristics." In Numerical Methods Using Kotlin, 473–94. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8826-9_11.

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Hromkovič, Juraj. "Heuristics." In Texts in Theoretical Computer Science An EATCS Series, 387–415. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04616-6_6.

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Hromkovič, Juraj. "Heuristics." In Texts in Theoretical Computer Science. An EATCS Series, 431–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-05269-3_6.

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Conference papers on the topic "Heuristics"

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Hitomi, Nozomi, and Daniel Selva. "The Effect of Credit Definition and Aggregation Strategies on Multi-Objective Hyper-Heuristics." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-47445.

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Heuristics and meta-heuristics are often used to solve complex real-world problems such as the non-linear, non-convex, and multi-objective combinatorial optimization problems that regularly appear in system design and architecture. Unfortunately, the performance of a specific heuristic is largely dependent on the specific problem at hand. Moreover, a heuristic’s performance can vary throughout the optimization process. Hyper-heuristics is one approach that can maintain relatively good performance over the course of an optimization process and across a variety of problems without parameter retuning or major modifications. Given a set of domain-specific and domain-independent heuristics, a hyper-heuristic adapts its search strategy over time by selecting the most promising heuristics to use at a given point. A hyper-heuristic must have: 1) a credit assignment strategy to rank the heuristics by their likelihood of producing improving solutions; and 2) a heuristic selection strategy based on the credits assigned to each heuristic. The literature contains many examples of hyper-heuristics with effective credit assignment and heuristic selection strategies for single-objective optimization problems. In multi-objective optimization problems, however, defining credit is less straightforward because there are often competing objectives. Therefore, there is a need to define and assign credit so that heuristics are rewarded for finding solutions with good trades between the objectives. This paper studies, for the first time, different combinations of credit definition, credit aggregation, and heuristic selection strategies. Credit definitions are based on different applications of the notion of Pareto dominance, namely: A1) dominance of the offspring with respect to the parent solutions; A2) ability to produce non-dominated solutions with respect to the entire population; A3) Pareto ranking with respect to the entire population. Two different credit aggregation strategies for assigning credit are also examined. A heuristic will receive credit for: B1) only the solutions it created in the current iteration or B2) all solutions it created that are in the current population. Different heuristic selection strategies are considered including: C1) probability matching; C2) dynamic multi-armed bandit; and C3) Hyper-GA. Thus, we conduct an experiment with three factors: credit definition (A1, A2, A3), credit aggregation (B1, B2), and heuristic selection (C1, C2, C3) and conduct a full factorial experiment. Performance is measured by hyper-volume of the last population. All algorithms are tested on a design problem for a climate monitoring satellite constellation instead of classical benchmarking problems to apply domain-specific heuristics within the hyper-heuristic.
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Trevizan, Felipe, Sylvie Thiébaux, and Patrik Haslum. "Operator Counting Heuristics for Probabilistic Planning." 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.2018/758.

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For the past 25 years, heuristic search has been used to solve domain-independent probabilistic planning problems, but with heuristics that determinise the problem and ignore precious probabilistic information. In this paper, we present a generalization of the operator-counting family of heuristics to Stochastic Shortest Path problems (SSPs) that is able to represent the probability of the actions outcomes. Our experiments show that the equivalent of the net change heuristic in this generalized framework obtains significant run time and coverage improvements over other state-of-the-art heuristics in different planners.
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Wichlacz, Julia, Daniel Höller, and Jörg Hoffmann. "Landmark Heuristics for Lifted Classical Planning." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/647.

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While state-of-the-art planning systems need a grounded (propositional) task representation, the input model is provided "lifted", specifying predicates and action schemas with variables over a finite object universe. The size of the grounded model is exponential in predicate/action-schema arity, limiting applicability to cases where it is small enough. Recent work has taken up this challenge, devising an effective lifted forward search planner as basis for lifted heuristic search, as well as a variety of lifted heuristic functions based on the delete relaxation. Here we add a novel family of lifted heuristic functions, based on landmarks. We design two methods for landmark extraction in the lifted setting. The resulting heuristics exhibit performance advantages over previous heuristics in several benchmark domains. Especially the combination with lifted delete relaxation heuristics to a LAMA-style planner yields good results, beating the previous state of the art in lifted planning.
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Renan de Carvalho, Vinicius, and Jaime Simão Sichman. "Multi-Agent Election-Based Hyper-Heuristics." 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.2018/833.

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Hyper-heuristics are high-level methodologies responsible for automatically discover how to combine elements from a low-level heuristic set in order to solve optimization problems. Agents, in turn, are autonomous component responsible for watching an environment and perform some actions according to their perceptions. Thus, agent-based techniques seem suitable for the design of hyper-heuristics. This work presents an agent-based hyper-heuristic framework for choosing the best low-level heuristic. The proposed framework performs a cooperative voting procedure, considering a set of quality indicator voters, to define which multi-objective evolutionary algorithm (MOEA) should generate more new solutions along the execution.
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Puentes, Lucas, Jonathan Cagan, and Christopher McComb. "Automated Heuristic Induction From Human Design Data." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22151.

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Abstract Through experience, designers develop guiding principles, or heuristics, to aid decision-making in familiar design domains. Generalized versions of common design heuristics have been identified across multiple domains and applied by novices to design problems. Previous work leveraged a sample of these common heuristics to assist in an agent-based design process, which typically lacks heuristics. These predefined heuristics were translated into sequences of specifically applied design changes that followed the theme of the heuristic. To overcome the upfront burden, need for human interpretation, and lack of generality of this manual process, this paper presents a methodology that induces frequent heuristic sequences from an existing timeseries design change dataset. Individual induced sequences are then algorithmically grouped based on similarity to form groups that each represent a shared general heuristic. The heuristic induction methodology is applied to data from two human design studies in different design domains. The first dataset, collected from a truss design task, finds a highly similar set of general heuristics used by human designers to that which was hand selected for the previous computational agent study. The second dataset, collected from a cooling system design problem, demonstrates further applicability and generality of the heuristic induction process. Through this heuristic induction technique, designers working in a specified domain can learn from others’ prior problem-solving strategies and use these strategies in their own future design problems.
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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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Mischek, Florian, and Nysret Musliu. "Reinforcement Learning for Cross-Domain Hyper-Heuristics." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/664.

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In this paper, we propose a new hyper-heuristic approach that uses reinforcement learning to automatically learn the selection of low-level heuristics across a wide range of problem domains. We provide a detailed analysis and evaluation of the algorithm components, including different ways to represent the hyper-heuristic state space and reset strategies to avoid unpromising areas of the solution space. Our methods have been evaluated using HyFlex, a well-known benchmarking framework for cross-domain hyper-heuristics, and compared with state-of-the-art approaches. The experimental evaluation shows that our reinforcement-learning based approach produces results that are competitive with the state-of-the-art, including the top participants of the Cross Domain Hyper-heuristic Search Competition 2011.
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Interian, Yannet, and Sara Bernardini. "Learning Interpretable Heuristics for WalkSAT." In 20th International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/kr.2023/36.

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Local search algorithms are well-known methods for solving large, hard instances of the satisfiability problem (SAT). The performance of these algorithms crucially depends on heuristics for setting noise parameters and scoring variables. The optimal setting for these heuristics varies for different instance distributions. In this paper, we present an approach for learning effective variable scoring functions and noise parameters by using reinforcement learning. We consider satisfiability problems from different instance distributions and learn specialized heuristics for each of them. Our experimental results show improvements with respect to both a WalkSAT baseline and another local search learned heuristic.
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Hu, Shuli, and Nathan R. Sturtevant. "Direction-Optimizing Breadth-First Search with External Memory Storage." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/175.

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While computing resources have continued to grow, methods for building and using large heuristics have not seen significant advances in recent years. We have observed that direction-optimizing breadth-first search, developed for and used broadly in the Graph 500 competition, can also be applied for building heuristics. But, the algorithm cannot run efficiently using external memory -- when the heuristics being built are larger than RAM. This paper shows how to modify direction-optimizing breadth-first search to build external-memory heuristics. We show that the new approach is not effective in state spaces with low asymptotic branching factors, but in other domains we are able to achieve up to a 3x reducing in runtime when building an external-memory heuristic. The approach is then used to build a 2.6TiB Rubik's Cube heuristic with 5.8 trillion entries, the largest pattern database heuristic ever built.
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Fan, Gaojian, Martin Müller, and Robert Holte. "Additive Merge-and-Shrink Heuristics for Diverse Action Costs." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/599.

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In many planning applications, actions can have highly diverse costs. Recent studies focus on the effects of diverse action costs on search algorithms, but not on their effects on domain-independent heuristics. In this paper, we demonstrate there are negative impacts of action cost diversity on merge-and-shrink (M&S), a successful abstraction method for producing high-quality heuristics for planning problems. We propose a new cost partitioning method for M&S to address the negative effects of diverse action costs. We investigate non-unit cost IPC domains, especially those for which diverse action costs have severe negative effects on the quality of the M&S heuristic. Our experiments demonstrate that in these domains, an additive set of M&S heuristics using the new cost partitioning method produces much more informative and effective heuristics than creating a single M&S heuristic which directly encodes diverse costs.
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Reports on the topic "Heuristics"

1

Franklin, R., and L. Harmon. Heuristics for Cooperative Problem Solving. Fort Belvoir, VA: Defense Technical Information Center, February 1989. http://dx.doi.org/10.21236/ada206371.

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Pasch, Kenneth A. Heuristics for Job-Shop Scheduling. Fort Belvoir, VA: Defense Technical Information Center, January 1988. http://dx.doi.org/10.21236/ada198192.

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Kivinen, T., and D. McDonald. Heuristics for Detecting ESP-NULL Packets. RFC Editor, May 2010. http://dx.doi.org/10.17487/rfc5879.

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Simon, Luke. Visualization for Hyper-Heuristics: Back-End Processing. Office of Scientific and Technical Information (OSTI), March 2015. http://dx.doi.org/10.2172/1177600.

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Hooker, John N. Combining AI and OR in Heuristics and Optimization. Fort Belvoir, VA: Defense Technical Information Center, December 1997. http://dx.doi.org/10.21236/ada387176.

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Kroenung, Lauren. Visualization for Hyper-Heuristics: Front-End Graphical User Interface. Office of Scientific and Technical Information (OSTI), March 2015. http://dx.doi.org/10.2172/1177598.

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Harris, Sean. A Comparison of Genetic Programming Variants for Hyper-Heuristics. Office of Scientific and Technical Information (OSTI), March 2015. http://dx.doi.org/10.2172/1177599.

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Hughes, Jesse B. Hyper-Heuristics to Automatically Target Code to Computer Architectures. Office of Scientific and Technical Information (OSTI), March 2016. http://dx.doi.org/10.2172/1618239.

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McBride, Dorothy J., and Clifford E. Brown. Team Performance in Dynamic Decision Making: The Importance of Heuristics. Fort Belvoir, VA: Defense Technical Information Center, February 1989. http://dx.doi.org/10.21236/ada209618.

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Bixby, Robert E., and Robert Fourer. Finding Embedded Network Rows in Linear Programs I: Extraction Heuristics. Fort Belvoir, VA: Defense Technical Information Center, August 1986. http://dx.doi.org/10.21236/ada455195.

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