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Journal articles on the topic 'Heuristics methods'

Author: Grafiati

Published: 30 May 2022

Last updated: 25 July 2025

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1

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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Cichowicz, T., M. Drozdowski, M. Frankiewicz, G. Pawlak, F. Rytwinski, and J. Wasilewski. "Hyper-heuristics for cross-domain search." Bulletin of the Polish Academy of Sciences: Technical Sciences 60, no. 4 (2012): 801–8. http://dx.doi.org/10.2478/v10175-012-0093-7.

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Abstract In this paper we present two hyper-heuristics developed for the Cross-Domain Heuristic Search Challenge. Hyper-heuristics solve hard combinatorial problems by guiding low level heuristics, rather than by manipulating problem solutions directly. Two hyper-heuristics are presented: Five Phase Approach and Genetic Hive. Development paths of the algorithms and testing methods are outlined. Performance of both methods is studied. Useful and interesting experience gained in construction of the hyper-heuristics are presented. Conclusions and recommendations for the future advancement of hype

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Abdul-Razaq, Tariq, Hanan Chachan, and Faez Ali. "Modified Heuristics for Scheduling in Flow Shop to Minimize Makespan." Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 ), no. 2 (October 19, 2021): 1–20. http://dx.doi.org/10.55562/jrucs.v30i2.361.

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The NP-completeness of flow shops scheduling problems has been discussed for many years. Hence many heuristics have been proposed to obtain solutions of good quality with a small computational effort. The CDS (Campbell et al) and NEH (Nawaz, Enscore and Ham) heuristics are efficient among meta-heuristics such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).This paper discusses some methods and suggests new developing to the methods of the scheduling in flow shop to minimize makespan problems. Our main object in this paper, from one side, is to improve efficient heuristics which

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Raikhert, Kostiantyn. "THE PHILOSOPHICAL HEURISTICS OF J. HARTMAN AND YA. HNATIUK." Doxa, no. 2(38) (December 18, 2022): 38–51. http://dx.doi.org/10.18524/2410-2601.2022.2(38).283062.

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The study surveys of philosophical heuristics and centers on the philosophical heuristics of J. Hartman and Ya. Hnatiuk. J. Hartman’s philosophical heuristics studies philosophy in terms of its heuristic nature. J. Hartman implies that the entire philosophy is a set of heuristic projects: from conceptions to methods of philosophy. Even philosophical heuristics is one such project. Philosophical heuristics is a metaphilosophy, the specific distinction of which is to study the heuristic nature of philosophy. Philosophical heuristics of Ya. Hnatiuk is a methodology focused on the creativity of ph

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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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Liubarets, Vladyslava, and Alina Lyubyma. "Heuristic Teaching Methods Of Professional Junior Bachelors In Software Engineering." International Science Journal of Education & Linguistics 1, no. 2 (2022): 1–6. http://dx.doi.org/10.46299/j.isjel.20220102.1.

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The article theoretically substantiates the methods of heuristic training of professional bachelors in software engineering. The historical aspect of the origin of heuristics is realized. Scientific and methodological literature on this issue is analyzed. The main definitions of the heuristic learning theory are revealed. The meaning of the concepts: “heuristics”, “heuristic learning” is specified. The main methods and techniques of professional junior bachelors’ readiness formation of in software engineering for heuristic activity in the process of professional training are indicated. The app

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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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Muklason, Ahmad, Shof Rijal Ahlan Robbani, Edwin Riksakomara, and I. Gusti Agung Premananda. "A comparison of meta-heuristic and hyper-heuristic algorithms in solving an urban transit routing problems." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 2923. http://dx.doi.org/10.11591/ijai.v13.i3.pp2923-2933.

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<p class="p1">Public transport is a serious problem that is difﬁcult to solve in many countries. Public transport routing optimization problem also known as urban transit routing problem (UTRP) is time-consuming process, therefore effective approches are urgently needed. UTRP aims to minimize cost passenger and operator from a combination of route set. UTRP can be optimize with heuristics, meta-heuristics, and hyper-heuristics methods. In several previous studies, UTRP can be optimized with any meta-heuristics and hyper-heuristics methods. In this study we compare the performance of meta

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Ahmad, Muklason, Rijal Ahlan Robbani Shof, Riksakomara Edwin, and Gusti Agung Premananda I. "A comparison of meta-heuristic and hyper-heuristicalgorithms in solving an urban transit routing problems." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 2923–33. https://doi.org/10.11591/ijai.v13.i3.pp2923-2933.

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Public transport is a serious problem that is difficult to solve in many countries. Public transport routing optimization problem also known as urban transit routing problem (UTRP) is time-consuming process, therefore effective approches are urgently needed. UTRP aims to minimize cost passenger and operator from a combination of route set. UTRP can be optimize with heuristics, meta-heuristics, and hyper-heuristics methods. In several previous studies, UTRP can be optimized with any meta-heuristics and hyper-heuristics methods. In this study we compare the performance of meta-heuristic methods,

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Triki, C. "Solution Methods for the Periodic Petrol Station Replenishment Problem." Journal of Engineering Research [TJER] 10, no. 2 (2013): 69. http://dx.doi.org/10.24200/tjer.vol10iss2pp69-77.

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In this paper we introduce the Periodic Petrol Station Replenishment Problem (PPSRP) over a T-day planning horizon and describe four heuristic methods for its solution. Even though all the proposed heuristics belong to the common partitioning-then-routing paradigm, they differ in assigning the stations to each day of the horizon. The resulting daily routing problems are then solved exactly until achieving optimalization. Moreover, an improvement procedure is also developed with the aim of ensuring a better quality solution. Our heuristics are tested and compared in two real-life cases, and our

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Gonzalez-Holland, Emily, Daphne Whitmer, Larry Moralez, and Mustapha Mouloua. "Examination of the Use of Nielsen’s 10 Usability Heuristics & Outlooks for the Future." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1 (2017): 1472–75. http://dx.doi.org/10.1177/1541931213601853.

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Heuristics are commonly employed throughout various stages of the design process to evaluate the usability of interfaces. Heuristic Evaluation (HE) provides researchers with a cost effective and practical means to effectively assess designs. In this article, we aim to outline the development and application of one of the most frequently cited set of heuristic evaluation tools, Nielsen’s (1994) 10 usability heuristics. Nielsen’s heuristics have not only been applied to various modalities of interface design, but have also been compared to other usability evaluation methods. Moreover, in many ca

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Seipp, Jendrik, and Malte Helmert. "Diverse and Additive Cartesian Abstraction Heuristics." Proceedings of the International Conference on Automated Planning and Scheduling 24 (May 11, 2014): 289–97. http://dx.doi.org/10.1609/icaps.v24i1.13639.

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We have recently shown how counterexample-guided abstraction refinement can be used to derive informative Cartesian abstraction heuristics for optimal classical planning. In this work we introduce two methods for producing diverse sets of heuristics within this framework, one based on goal facts, the other based on landmarks. In order to sum the heuristic estimates admissibly we present a novel way of finding cost partitionings for explicitly represented abstraction heuristics. We show that the resulting heuristics outperform other state-of-the-art abstraction heuristics on many benchmark doma

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Парфентьєва І. П. та Матвійчук К. О. "ЕТИМОЛОГІЯ ПОНЯТТЯ “ЕВРИСТИЧНИЙ ПІДХІД”". World Science 3, № 8(36) (2018): 46–49. http://dx.doi.org/10.31435/rsglobal_ws/30082018/6078.

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   The article deals with the term "heuristics", which in scientific knowledge has the designation as a "method of finding." The author conducts an analysis of scientific literature on the interpretation of heuristics as a scientific concept. Heuristics as an independent science has not been fully formed yet. Despite the large number of scientific papers devoted to questions of heuristics, they usually relate to its particular problems and do not give a clear idea of either the object or subject of heuristics or its status among other sciences. It is suggested that pedagogica

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Soria-Alcaraz, Jorge A., Gabriela Ochoa, Andres Espinal, Marco A. Sotelo-Figueroa, Manuel Ornelas-Rodriguez, and Horacio Rostro-Gonzalez. "A Methodology for Classifying Search Operators as Intensification or Diversification Heuristics." Complexity 2020 (February 13, 2020): 1–10. http://dx.doi.org/10.1155/2020/2871835.

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Selection hyper-heuristics are generic search tools that dynamically choose, from a given pool, the most promising operator (low-level heuristic) to apply at each iteration of the search process. The performance of these methods depends on the quality of the heuristic pool. Two types of heuristics can be part of the pool: diversification heuristics, which help to escape from local optima, and intensification heuristics, which effectively exploit promising regions in the vicinity of good solutions. An effective search strategy needs a balance between these two strategies. However, it is not str

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CHANG, KAI H., JAMES H. CROSS II, W. HOMER CARLISLE, and SHIH-SUNG LIAO. "A PERFORMANCE EVALUATION OF HEURISTICS-BASED TEST CASE GENERATION METHODS FOR SOFTWARE BRANCH COVERAGE." International Journal of Software Engineering and Knowledge Engineering 06, no. 04 (1996): 585–608. http://dx.doi.org/10.1142/s0218194096000247.

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Software testing is an important step in the development of complex systems. The construction of test cases using traditional methods usually requires considerable manual effort. QUEST/Ada—Query Utility Environment for Software Testing of Ada, is a prototype test case generation system that uses various heuristics-based approaches to generate test cases. The system, which is designed for unit testing, generates test cases by monitoring the branch coverage progress and intelligently modifying existing test cases to achieve additional coverage. Three heuristics-based approaches along with a rand

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Veerapaneni, Rishi, Muhammad Suhail Saleem, and Maxim Likhachev. "Learning Local Heuristics for Search-Based Navigation Planning." Proceedings of the International Conference on Automated Planning and Scheduling 33, no. 1 (2023): 634–38. http://dx.doi.org/10.1609/icaps.v33i1.27245.

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Graph search planning algorithms for navigation typically rely heavily on heuristics to efficiently plan paths. As a result, while such approaches require no training phase and can directly plan long horizon paths, they often require careful hand designing of informative heuristic functions. Recent works have started bypassing hand designed heuristics by using machine learning to learn heuristic functions that guide the search algorithm. While these methods can learn complex heuristic functions from raw input, they i) require significant training and ii) do not generalize well to new maps and

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West, Todd, John Sessions, and Bogdan M. Strimbu. "Heuristic Optimization of Thinning Individual Douglas-Fir." Forests 12, no. 3 (2021): 280. http://dx.doi.org/10.3390/f12030280.

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Research Highlights: (1) Optimizing mid-rotation thinning increased modeled land expectation values by as much as 5.1–10.1% over a representative reference prescription on plots planted at 2.7 and 3.7 m square spacings. (2) Eight heuristics, five of which were newly applied to selecting individual trees for thinning, produced thinning prescriptions of near identical quality. (3) Based on heuristic sampling properties, we introduced a variant of the hero heuristic with a 5.3–20% greater computational efficiency. Background and Objectives: Thinning, which is arguably the most subjective human in

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Mazurkiewicz, Małgorzata. "Heuristic methods as a powerful managerial tool for enterprise in times of uncertainty." Kwartalnik Nauk o Przedsiębiorstwie 63, no. 1 (2022): 57–70. http://dx.doi.org/10.33119/knop.2022.63.1.5.

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In managing enterprise, uncertainty belongs to the attributes of the decision making processes. In competitive environments, where success depends on high-quality decisions, taking the wrong steps can lead to loss of market share or even contribute to running out of business. That is why it is important to choose from the many alternatives those methods which will be most effective in solving problems in a simple and quick way, and heuristics offer such a solution, which has proved its usefulness in uncertain business environments. The aim of this study is to present and evaluate heuristic met

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Barley, Michael, Santiago Franco, and Pat Riddle. "Overcoming the Utility Problem in Heuristic Generation: Why Time Matters." Proceedings of the International Conference on Automated Planning and Scheduling 24 (May 10, 2014): 38–46. http://dx.doi.org/10.1609/icaps.v24i1.13627.

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Progress has been made recently in developing tech- niques to automatically generate effective heuristics. These techniques typically aim to reduce the size of the search tree, usually by combining more primitive heuristics. However, simply reducing search tree size is not enough to guarantee that problems will be solved more quickly. We describe a new approach to auto- matic heuristic generation that combines more primi- tive heuristics in a way that can produce better heuris- tics than current methods. We report on experiments us- ing 14 planning domains that show our system leads to a much

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Yao, Shunyu, Fei Liu, Xi Lin, Zhichao Lu, Zhenkun Wang, and Qingfu Zhang. "Multi-Objective Evolution of Heuristic Using Large Language Model." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 25 (2025): 27144–52. https://doi.org/10.1609/aaai.v39i25.34922.

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Heuristics are commonly used to tackle various search and optimization problems. Design heuristics usually require tedious manual crafting with domain knowledge. Recent works have incorporated Large Language Models (LLMs) into automatic heuristic search, leveraging their powerful language and coding capacity. However, existing research focuses on the optimal performance on the target problem as the sole objective, neglecting other criteria such as efficiency and scalability, which are vital in practice. To tackle this challenge, we propose to model the heuristic search as a multi-objective opt

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Okitonyumbe Y. F, Joseph, Berthold Ulungu E.-L, and Joel Kapiamba Nt. "Cobweb heuristic for multi-objective vehicle routing problem." International Journal of Applied Mathematical Research 4, no. 3 (2015): 430. http://dx.doi.org/10.14419/ijamr.v4i3.4317.

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<p>Solving a classical vehicle routing problem (VRP) by exact methods presents many difficulties for large dimension problem. Consequently, in multi-objective framework, heuristic or metaheuristic methods are required. Due to particular VRP structure, it seems that a dedicated heuristicis more suitable than a metaheuristic. The aim of this article is to collapse different heuristics solving classical VRP and adapt them for to solve the multi-objective vehicle routing problem (MOVRP). The so-called Cobweb Algorithm simulates spider’s behavior when weaving cobweb. This paper presents the a

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Pickard, Andrew C., Richard Beasley, Dean Beale, et al. "Case Studies for Complexity Pattern Identification." INCOSE International Symposium 34, no. 1 (2024): 544–69. http://dx.doi.org/10.1002/iis2.13162.

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AbstractThe INCOSE Complex Systems Working Group Heuristics Team has selected 67 Principles and Heuristics that are considered to be particularly relevant to Complex Systems. These have been incorporated into a Difficulty Assessment Tool that prioritizes the list of Principles and Heuristics based on scoring of a matrix of four Difficulty Elements and six System Elements (to characterize the nature of the complexity).The purpose of this paper is to describe an initial assessment of the effectiveness and usefulness of the Difficulty Assessment Tool. The Tool has been used to assess eight Case S

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Mellouli, O., I. Hafidi, and A. Metrane. "A modified choice function hyper-heuristic with Boltzmann function." Mathematical Modeling and Computing 8, no. 4 (2021): 736–46. http://dx.doi.org/10.23939/mmc2021.04.736.

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Hyper-heuristics are a subclass of high-level research methods that function in a low-level heuristic research space. Their aim objective is to improve the level of generality for solving combinatorial optimization problems using two main components: a methodology for the heuristic selection and a move acceptance criterion, to ensure intensification and diversification [1]. Thus, rather than working directly on the problem's solutions and selecting one of them to proceed to the next step at each stage, hyper-heuristics operates on a low-level heuristic research space. The choice function is on

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Özçağdavul, Mazlum. "A COMPREHENSIVE ANALYSIS OF MULTI-STRATEGY MEMETIC ALGORITHMS INCORPORATING LOW-LEVEL HEURISTICS AND ACCEPTANCE MECHANISMS." AYBU Business Journal 4, no. 1 (2024): 1–23. http://dx.doi.org/10.61725/abj.1499654.

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Hyper-heuristics are designed to be reusable, domain-independent methods for addressing complex computational issues. While there are specialized approaches that work well for particular problems, they often require parameter tuning and cannot be transferred to other problems. Memetic Algorithms combine genetic algorithms and local search techniques. The evolutionary interaction of memes allows for the creation of intelligent complexes capable of solving computational problems. Hyper-heuristics are a high-level search technique that operates on a set of low-level heuristics that directly addre

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Šimon, Marek, Ladislav Huraj, Iveta Dirgová Luptáková, and Jiří Pospíchal. "Heuristics for Spreading Alarm throughout a Network." Applied Sciences 9, no. 16 (2019): 3269. http://dx.doi.org/10.3390/app9163269.

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This paper provides heuristic methods for obtaining a burning number, which is a graph parameter measuring the speed of the spread of alarm, information, or contagion. For discrete time steps, the heuristics determine which nodes (centers, hubs, vertices, users) should be alarmed (in other words, burned) and in which order, when afterwards each alarmed node alarms its neighbors in the network at the next time step. The goal is to minimize the number of discrete time steps (i.e., time) it takes for the alarm to reach the entire network, so that all the nodes in the networks are alarmed. The bur

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Felner, A., R. E. Korf, and S. Hanan. "Additive Pattern Database Heuristics." Journal of Artificial Intelligence Research 22 (November 1, 2004): 279–318. http://dx.doi.org/10.1613/jair.1480.

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We explore a method for computing admissible heuristic evaluation functions for search problems. It utilizes pattern databases, which are precomputed tables of the exact cost of solving various subproblems of an existing problem. Unlike standard pattern database heuristics, however, we partition our problems into disjoint subproblems, so that the costs of solving the different subproblems can be added together without overestimating the cost of solving the original problem. Previously, we showed how to statically partition the sliding-tile puzzles into disjoint groups of tiles to compute an ad

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Chen, Wenlin, Yixin Chen, Kilian Weinberger, Qiang Lu, and Xiaoping Chen. "Goal-Oriented Euclidean Heuristics with Manifold Learning." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (2013): 173–79. http://dx.doi.org/10.1609/aaai.v27i1.8615.

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Recently, a Euclidean heuristic (EH) has been proposed for A* search. EH exploits manifold learning methods to construct an embedding of the state space graph, and derives an admissible heuristic distance between two states from the Euclidean distance between their respective embedded points. EH has shown good performance and memory efficiency in comparison to other existing heuristics such as differential heuristics. However, its potential has not been fully explored. In this paper, we propose a number of techniques that can significantly improve the quality of EH. We propose a goal-oriented

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Pothitos, Nikolaos, and Panagiotis Stamatopoulos. "Building Search Methods with Self-Confidence in a Constraint Programming Library." International Journal on Artificial Intelligence Tools 27, no. 04 (2018): 1860003. http://dx.doi.org/10.1142/s0218213018600035.

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In the late 1990s, Constrained Programming (CP) promised to separate the declaration of a problem from the process to solve it. This work attempts to serve this direction, by implementing and presenting a modular way to define search methods that seek solutions to arbitrary Constraint Satisfaction Problems (CSPs). The user just declares their CSP, and it can be solved using a portfolio of search methods already in place. Apart from the pluggable search methods framework for any CSP, we also introduce pluggable heuristics for our search methods. We found an efficient stochastic heuristics’ para

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Khowaja, Kamran, and Dena Al-Thani. "New Checklist for the Heuristic Evaluation of mHealth Apps (HE4EH): Development and Usability Study." JMIR mHealth and uHealth 8, no. 10 (2020): e20353. http://dx.doi.org/10.2196/20353.

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Background Diabetes is one of the leading causes of death in developing countries. Existing mobile health (mHealth) app design guidelines lack a description of the support of continuous self-monitoring of health status, behavior change to improve and adopt a healthy lifestyle, and communication with health educators and health care professionals in case of any need. Objective This paper presents the development of a specialized set of heuristics called heuristic evaluation for mHealth apps (HE4EH) as an all-in-one tool and its applicability by performing a heuristic evaluation of an mHealth ap

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Jackovich, Petar, Bruce Cox, and Raymond R. Hill. "Comparing greedy constructive heuristic subtour elimination methods for the traveling salesman problem." Journal of Defense Analytics and Logistics 4, no. 2 (2020): 167–82. http://dx.doi.org/10.1108/jdal-09-2020-0018.

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Purpose This paper aims to define the class of fragment constructive heuristics used to compute feasible solutions for the traveling salesman problem (TSP) into edge-greedy and vertex-greedy subclasses. As these subclasses of heuristics can create subtours, two known methodologies for subtour elimination on symmetric instances are reviewed and are expanded to cover asymmetric problem instances. This paper introduces a third novel subtour elimination methodology, the greedy tracker (GT), and compares it to both known methodologies. Design/methodology/approach Computational results for all three

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Murtza, Rabia, Stephen Monroe, and Robert J. Youmans. "Heuristic Evaluation for Virtual Reality Systems." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1 (2017): 2067–71. http://dx.doi.org/10.1177/1541931213602000.

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The advent of Virtual Reality (VR) has created new user-interaction paradigms that VR designers need to attend to in order to avoid usability issues. Currently, there are few formal methods for evaluating the usability of VR interfaces. In this paper, we introduce a new set of heuristics that can be used to carry out usability inspections of VR systems via the Heuristic Evaluation method. The heuristics were developed to identify usability problems in both developing and currently-existing VR hardware and software. The heuristics reported here were developed by surveying VR users, and then usi

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Peterson, John R., Anagha Kulkarni, Emil Keyder, Joseph Kim, and Shlomo Zilberstein. "Trajectory Constraint Heuristics for Optimal Probabilistic Planning." Proceedings of the International Symposium on Combinatorial Search 15, no. 1 (2022): 153–61. http://dx.doi.org/10.1609/socs.v15i1.21763.

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Search algorithms such as LAO* and LRTDP coupled with admissible heuristics are widely used methods for optimal probabilistic planning. Their effectiveness depends on the degree to which heuristics are able to approximate the optimal cost of a state. Most common domain-independent heuristics, however, rely on determinization, and ignore the probabilities associated with different effects of actions. Here, we present a method for decomposing a probabilistic planning problem into subproblems by constraining possible action outcomes. Admissible heuristics evaluated for each subproblem can then be

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Sievers, Silvan, Martin Wehrle, Malte Helmert, and Michael Katz. "Strengthening Canonical Pattern Databases with Structural Symmetries." Proceedings of the International Symposium on Combinatorial Search 8, no. 1 (2021): 91–99. http://dx.doi.org/10.1609/socs.v8i1.18429.

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Symmetry-based state space pruning techniques have proved to greatly improve heuristic search based classical planners. Similarly, abstraction heuristics in general and pattern databases in particular are key ingredients of such planners. However, only little work has dealt with how the abstraction heuristics behave under symmetries. In this work, we investigate the symmetry properties of the popular canonical pattern databases heuristic. Exploiting structural symmetries, we strengthen the canonical pattern databases by adding symmetric pattern databases, making the resulting heuristic invaria

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Drăghici, Marius Augustin. "Heuristic Aspects Concerning Mathematics in Kant’s Work." Revue roumaine de philosophie 68, no. 2 (2024): 559–605. https://doi.org/10.59277/rrp.2024.68.2.18.

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This essay explores the intersection of Kant’s engagement with mathematics and its heuristic dimensions in his (pre)critical theoretic philosophy. It examines Kant’s approach to mathematical methods – synthesis and analysis – through four interconnected sections: highlighting the role of heuristics as problem-solving method, the first section connects heuristics to the ancient mathematical methods and their role in transcendental philosophy; the second section investigates Kant’s pre-critical adoption of the synthetic method and the problem of its imitation in (transcendental) philosophy; the

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Davidov, D., and S. Markovitch. "Multiple-Goal Heuristic Search." Journal of Artificial Intelligence Research 26 (August 25, 2006): 417–51. http://dx.doi.org/10.1613/jair.1940.

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This paper presents a new framework for anytime heuristic search where the task is to achieve as many goals as possible within the allocated resources. We show the inadequacy of traditional distance-estimation heuristics for tasks of this type and present alternative heuristics that are more appropriate for multiple-goal search. In particular, we introduce the marginal-utility heuristic, which estimates the cost and the benefit of exploring a subtree below a search node. We developed two methods for online learning of the marginal-utility heuristic. One is based on local similarity of the part

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Danach, Kassem, Hassan Harb, Semaan Amine, and Mariem Belhor. "NeuHH: A Neuromorphic-Inspired Hyper-Heuristic Framework for Solving the Capacitated Single-Allocation p-Hub Location Routing Problem." Vehicles 7, no. 2 (2025): 61. https://doi.org/10.3390/vehicles7020061.

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This paper introduces a novel neuromorphic-inspired hyper-heuristic framework (NeuHH) for solving the Capacitated Single-Allocation p-Hub Location Routing Problem (CSAp-HLRP), a challenging combinatorial optimization problem that jointly addresses hub location decisions, capacity constraints, and vehicle routing. The proposed framework employs Spiking Neural Networks (SNNs) as the decision-making core, leveraging their temporal dynamics and spike-timing-dependent plasticity (STDP) to guide the real-time selection and adaptation of low-level heuristics. Unlike conventional learning-based hyper-

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Czerniachowska, K. S., and S. A. Subbotin. "OPTIMIZATION BASED ON FLOWER CUTTING HEURISTICS FOR SPACE ALLOCATION PROBLEM." Radio Electronics, Computer Science, Control, no. 2 (June 29, 2025): 196–208. https://doi.org/10.15588/1607-3274-2025-2-17.

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Context. This research discusses the shelf space allocation problem with vertical and horizontal product categorization, which also includes the products of general and brand assortment as well as products with different storage conditions stored on different shelves and incompatible products stored on the same shelf but no nearby.Objective. The goal is to maximize the profit, product movement, or sales after allocating products on store shelves, defining the shelf for the product and the number of stock-keeping units it has.Method. The research proposes the two variants of heuristics with dif

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Tarraq, Ali, Faissal Elmariami, Aziz Belfqih, and Touria Haidi. "Meta-heuristic optimization methods applied to renewable distributed generation planning: A review." E3S Web of Conferences 234 (2021): 00086. http://dx.doi.org/10.1051/e3sconf/202123400086.

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Due to its proven efficiency and computational speed, the most recent developed meta-heuristic optimization methods are widely used to better integrate renewable distributed generation (RDG) into the electricity grid. The main objective of this paper is to obtain a better knowledge of current trends in meta-heuristics applied to optimally integrate RDGs to the distribution network. This is a review of well known meta-heuristic approaches, used to solve the problem of optimal renewable distributed generation allocation planning (ORDGAP). In this context, some research gaps were mentioned, and r

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Sam, Dylan, and J. Zico Kolter. "Losses over Labels: Weakly Supervised Learning via Direct Loss Construction." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 9695–703. http://dx.doi.org/10.1609/aaai.v37i8.26159.

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Owing to the prohibitive costs of generating large amounts of labeled data, programmatic weak supervision is a growing paradigm within machine learning. In this setting, users design heuristics that provide noisy labels for subsets of the data. These weak labels are combined (typically via a graphical model) to form pseudolabels, which are then used to train a downstream model. In this work, we question a foundational premise of the typical weakly supervised learning pipeline: given that the heuristic provides all “label” information, why do we need to generate pseudolabels at all? Instead, we

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Kappesser, J., and A. C. de C Williams. "Clinical judgement heuristics: Methods and models." European Journal of Pain 17, no. 10 (2013): 1423–24. http://dx.doi.org/10.1002/j.1532-2149.2013.00392.x.

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Gárate-Escamilla, Anna Karen, Ivan Amaya, Jorge M. Cruz-Duarte, Hugo Terashima-Marín, and José Carlos Ortiz-Bayliss. "Identifying Hyper-Heuristic Trends through a Text Mining Approach on the Current Literature." Applied Sciences 12, no. 20 (2022): 10576. http://dx.doi.org/10.3390/app122010576.

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Hyper-heuristics have arisen as methods that increase the generality of existing solvers. They have proven helpful for dealing with complex problems, particularly those related to combinatorial optimization. Their recent growth in popularity has increased the daily amount of text in the related literature. This information is primarily unstructured, mainly text that traditional computer data systems cannot process. Traditional systematic literature review studies exhibit multiple limitations, including high time consumption, lack of replicability, and subjectivity of the results. For this reas

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Dr., O. Mahesh, Narasimha Rao M., Vivek Sindhoor B., Pavan Kalyan R., and Mohan Krishna K. "Job Shop Scheduling Using Bat Algorithm." Journal of Advanced Research in Industrial Engineering 5, no. 1 (2023): 1–8. https://doi.org/10.5281/zenodo.7821275.

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<em>The Job shop scheduling problems are used to identify the time at which each operation is to be processed on each machine. Then the calculation becomes more complicated. There are many methods available for solving the Job Shop Scheduling Problems (JSSP). Among these some are based on job priority rules and others are based on combinatorial optimization using meta-heuristics. Hence many single pass heuristics were developed to get a reasonably good solution. But these methods are applicable only for a single objective. Later on many Meta-heuristics were developed. These meta-heuristics are

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Cabrera-Guerrero, Guillermo, Carolina Lagos, Carolina Castañeda, Franklin Johnson, Fernando Paredes, and Enrique Cabrera. "Parameter Tuning for Local-Search-Based Matheuristic Methods." Complexity 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/1702506.

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Algorithms that aim to solve optimisation problems by combining heuristics and mathematical programming have attracted researchers’ attention. These methods, also known as matheuristics, have been shown to perform especially well for large, complex optimisation problems that include both integer and continuous decision variables. One common strategy used by matheuristic methods to solve such optimisation problems is to divide the main optimisation problem into several subproblems. While heuristics are used to seek for promising subproblems, exact methods are used to solve them to optimality. I

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PETIT, JORDI. "COMBINING SPECTRAL SEQUENCING AND PARALLEL SIMULATED ANNEALING FOR THE MINLA PROBLEM." Parallel Processing Letters 13, no. 01 (2003): 77–91. http://dx.doi.org/10.1142/s0129626403001161.

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In this paper we present and analyze new sequential and parallel heuristics to approximate the Minimum Linear Arrangement problem (MinLA). The heuristics consist in obtaining a first global solution using Spectral Sequencing and improving it locally through Simulated Annealing. In order to accelerate the annealing process, we present a special neighborhood distribution that tends to favor moves with high probability to be accepted. We show how to make use of this neighborhood to parallelize the Metropolis stage on distributed memory machines by mapping partitions of the input graph to processo

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Alshamari, Majed A., and Maha M. Althobaiti. "Usability Evaluation of Wearable Smartwatches Using Customized Heuristics and System Usability Scale Score." Future Internet 16, no. 6 (2024): 204. http://dx.doi.org/10.3390/fi16060204.

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The mobile and wearable nature of smartwatches poses challenges in evaluating their usability. This paper presents a study employing customized heuristic evaluation and use of the system usability scale (SUS) on four smartwatches, along with their mobile applications. A total of 11 heuristics were developed and validated by experts by combining Nielsen’s heuristic and Motti and Caines’ heuristics. In this study, 20 participants used the watches and participated in the SUS survey. A total of 307 usability issues were reported by the evaluators. The results of this study show that the Galaxy Wat

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Keyder, Emil, Joerg Hoffmann, and Patrik Haslum. "Semi-Relaxed Plan Heuristics." Proceedings of the International Conference on Automated Planning and Scheduling 22 (May 14, 2012): 128–36. http://dx.doi.org/10.1609/icaps.v22i1.13496.

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Heuristics based on the delete relaxation are at the forefront of modern domain-independent planning techniques. Here we introduce a principled and flexible technique for augmenting delete-relaxed tasks with a limited amount of delete information, by introducing special fluents that explicitly represent conjunctions of fluents in the original planning task. Differently from previous work in this direction, conditional effects are used to limit the growth of the task to be linear, rather than exponential, in the number of conjunctions that are introduced, making its use for obtaining heuristic

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Tüű-Szabó, Boldizsár, Péter Földesi, and László T. Kóczy. "An Efficient Tour Construction Heuristic for Generating the Candidate Set of the Traveling Salesman Problem with Large Sizes." Mathematics 12, no. 19 (2024): 2960. http://dx.doi.org/10.3390/math12192960.

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In this paper, we address the challenge of creating candidate sets for large-scale Traveling Salesman Problem (TSP) instances, where choosing a subset of edges is crucial for efficiency. Traditional methods for improving tours, such as local searches and heuristics, depend greatly on the quality of these candidate sets but often struggle in large-scale situations due to insufficient edge coverage or high time complexity. We present a new heuristic based on fuzzy clustering, designed to produce high-quality candidate sets with nearly linear time complexity. Thoroughly tested on benchmark instan

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Lavasani, Sepehr, Lior Siag, Shahaf S. Shperberg, Ariel Felner, and Nathan R. Sturtevant. "Anchor Search: A Unified Framework for Suboptimal Bidirectional Search." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 25 (2025): 27045–53. https://doi.org/10.1609/aaai.v39i25.34911.

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In recent years the understanding of optimal bidirectional heuristic search (BiHS) has progressed significantly. Yet, Bi-HS is relatively unexplored in unbounded suboptimal search. Front-to-end (F2E) and front-to-front (F2F) bidirectional search have been used in optimal algorithms, but adapting them for unbounded suboptimal search remains an open challenge. We introduce a framework for suboptimal BiHS, called anchor search, and use it to derive a parameterized family of algorithms. Because our new algorithms need F2F heuristic evaluations, we propose using pattern databases (PDBs) as differen

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Dimaguila, Gerardo Luis, Kathleen Gray, and Mark Merolli. "Enabling Better Use of Person-Generated Health Data in Stroke Rehabilitation Systems: Systematic Development of Design Heuristics." Journal of Medical Internet Research 22, no. 7 (2020): e17132. http://dx.doi.org/10.2196/17132.

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Background An established and well-known method for usability assessment of various human-computer interaction technologies is called heuristic evaluation (HE). HE has been adopted for evaluations in a wide variety of specialized contexts and with objectives that go beyond usability. A set of heuristics to evaluate how health information technologies (HITs) incorporate features that enable effective patient use of person-generated health data (PGHD) is needed in an era where there is a growing demand and variety of PGHD-enabled technologies in health care and where a number of remote patient-m

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Dang, Yinglong, Xiaoguang Gao, and Zidong Wang. "Choice Function-Based Hyper-Heuristics for Causal Discovery under Linear Structural Equation Models." Biomimetics 9, no. 6 (2024): 350. http://dx.doi.org/10.3390/biomimetics9060350.

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Causal discovery is central to human cognition, and learning directed acyclic graphs (DAGs) is its foundation. Recently, many nature-inspired meta-heuristic optimization algorithms have been proposed to serve as the basis for DAG learning. However, a single meta-heuristic algorithm requires specific domain knowledge and empirical parameter tuning and cannot guarantee good performance in all cases. Hyper-heuristics provide an alternative methodology to meta-heuristics, enabling multiple heuristic algorithms to be combined and optimized to achieve better generalization ability. In this paper, we

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