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Academic literature on the topic 'Heuristics'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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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 (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

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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

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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 (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 gene

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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 (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

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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 standar

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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 (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 de

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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 (rea

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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 (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

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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 (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

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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-vari

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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 lit

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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.<br>Includes bibliographical references (p. 121-128).<br>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 o

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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 random

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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

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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

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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

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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

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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 tra

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Books on the topic "Heuristics"

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

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Andre, Carlos, Reis Pinheiro, and Fiona McNeill, eds. Heuristics in Analytics. 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. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-07153-4.

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

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

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

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Chatfield, Tom, and Tom Chatfield. Heuristics and Cognitive Biases. SAGE Publications, Inc., 2022. http://dx.doi.org/10.4135/9781071880029.

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Talbi, El-Ghazali, and Amir Nakib, eds. Bioinspired Heuristics for Optimization. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-95104-1.

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French, Steven, and Harmke Kamminga, eds. Correspondence, Invariance and Heuristics. Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-017-1185-2.

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Rothlauf, Franz. Design of Modern Heuristics. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-540-72962-4.

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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. 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. 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. 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. 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. 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. 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. 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. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8826-9_11.

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Rubaltelli, Enrico. "Heuristics." In The Palgrave Encyclopedia of the Possible. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90913-0_1.

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

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Conference papers on the topic "Heuristics"

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Aoki, Yoichi, Keito Kudo, Tatsuki Kuribayashi, et al. "First Heuristic Then Rational: Dynamic Use of Heuristics in Language Model Reasoning." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-main.789.

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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 retu

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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}. 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 heurist

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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}. 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

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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 inter

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Núñez-Molina, Carlos, Masataro Asai, Pablo Mesejo, and Juan Fernandez-Olivares. "On Using Admissible Bounds for Learning Forward Search Heuristics." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/747.

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In recent years, there has been growing interest in utilizing modern machine learning techniques to learn heuristic functions for forward search algorithms. Despite this, there has been little theoretical understanding of what they should learn, how to train them, and why we do so. This lack of understanding has resulted in the adoption of diverse training targets (suboptimal vs optimal costs vs admissible heuristics) and loss functions (e.g., square vs absolute errors) in the literature. In this work, we focus on how to effectively utilize the information provided by admissible heuristics in

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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}. 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 indic

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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}. 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, an

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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}. 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 s

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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}. 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 exper

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Reports on the topic "Heuristics"

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

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Pasch, Kenneth A. Heuristics for Job-Shop Scheduling. Defense Technical Information Center, 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, 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), 2015. http://dx.doi.org/10.2172/1177600.

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Hooker, John N. Combining AI and OR in Heuristics and Optimization. Defense Technical Information Center, 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), 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), 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), 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. Defense Technical Information Center, 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. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada455195.

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