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Journal articles on the topic 'Empirical Evaluation'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Ma, Qing. "Empirical CALL evaluation." CALICO Journal 26, no. 1 (2013): 108–22. http://dx.doi.org/10.1558/cj.v26i1.108-122.

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CALL evaluation is important because it is the most efficient means to prove CALL effectiveness. While both learning process and learning outcome should be investigated in empirical evaluation, the precise relationship between the two needs to be examined closely. Only by doing so can we identify useful CALL design features that facilitate relevant user-computer interaction which can lead to an improved learning outcome. This study examined how certain user actions can affect or predict receptive/productive vocabulary retention in a computer-assisted vocabulary learning (CAVL) program. Results

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2

Thomsen, M., and L. Carlsen. "Evaluation of empirical versus non-empirical descriptors." SAR and QSAR in Environmental Research 13, no. 5 (2002): 525–40. http://dx.doi.org/10.1080/10629360290023359.

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3

Johnson, Nathan. "Empirical evaluation of monopolization." Critique 51, no. 1 (2023): 51–70. http://dx.doi.org/10.1080/03017605.2023.2238448.

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4

Raimondo, Estelle. "The power and dysfunctions of evaluation systems in international organizations." Evaluation 24, no. 1 (2018): 26–41. http://dx.doi.org/10.1177/1356389017749068.

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Evaluations do not take place in a vacuum. Evaluation systems are embedded within organizations; they shape and are shaped by organizational norms, processes, and behaviors. In International Organizations, evaluation systems are ubiquitous. Yet, little is known about how they “function,” namely how they are used, how they contribute to organizational performance, and how they influence actors’ behaviors. These are empirical questions that cannot be solved without a robust theoretical grounding, which is currently absent from the existing evaluation literature. This article seeks to bridge some

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5

Koubek, Tomáš, and David Procházka. "Empirical evaluation of augmented prototyping effectiveness." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 60, no. 2 (2012): 143–50. http://dx.doi.org/10.11118/actaun201260020143.

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Augmented reality is a scientific field well known for more than twenty years. Although there is a huge number of projects that present promising results, the real usage of augmented reality applications for fulfilling common tasks is almost negligible. We believe that one of the principal reasons is insufficient usability of these applications. The situation is analogous to the desktop, mobile or cloud application development or even to the web pages design. The first phase of a technology adoption is the exploration of its potential. As soon as the technical problems are overcome and the tec

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6

Raby, Caroline, Dean Alexis, Anthony Dickinson, and Nicola Clayton. "Empirical evaluation of mental time travel." Behavioral and Brain Sciences 30, no. 3 (2007): 330–31. http://dx.doi.org/10.1017/s0140525x07002166.

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AbstractAlthough the mental time travel (MTT) hypothesis provides a rich, conceptual framework, the absence of clear, empirically tractable, behavioural criteria for determining the capacity for MTT restricts its usefulness in comparative research. Examples of empirical criteria for evaluating MTT in animals are given. We also question the authors' evaluation of semantic foresight and their even-handedness in assessing human and nonhuman behaviour.

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Vial, Daniel, and Vijay Subramanian. "Empirical Policy Evaluation With Supergraphs." IEEE Journal on Selected Areas in Information Theory 2, no. 2 (2021): 641–51. http://dx.doi.org/10.1109/jsait.2021.3073257.

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8

McGill, Brian J., Brian A. Maurer, and Michael D. Weiser. "EMPIRICAL EVALUATION OF NEUTRAL THEORY." Ecology 87, no. 6 (2006): 1411–23. http://dx.doi.org/10.1890/0012-9658(2006)87[1411:eeont]2.0.co;2.

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9

Pagel, Mark D., and Clifford E. Lunneborg. "Empirical evaluation of ridge regression." Psychological Bulletin 97, no. 2 (1985): 342–55. http://dx.doi.org/10.1037/0033-2909.97.2.342.

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10

Riding, T. M. "Exploring values: an empirical evaluation." Journal of Learning Disabilities for Nursing, Health, and Social Care 1, no. 1 (1997): 25–30. http://dx.doi.org/10.1177/146900479700100105.

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11

Krivoruchko, Konstantin, and Alexander Gribov. "Evaluation of empirical Bayesian kriging." Spatial Statistics 32 (August 2019): 100368. http://dx.doi.org/10.1016/j.spasta.2019.100368.

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12

Komorowski, Ludwik. "Empirical evaluation of chemical hardness." Chemical Physics Letters 134, no. 6 (1987): 536–40. http://dx.doi.org/10.1016/0009-2614(87)87188-9.

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13

Flach, Peter. "Empirical Evaluation of Predictive Models." ACM SIGIR Forum 56, no. 1 (2022): 1–5. http://dx.doi.org/10.1145/3582524.3582528.

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I give a brief overview of my recent keynote at the 2022 European Conference on Information Retrieval that was held in Stavanger, Norway. I pay particular attention to some basic questions involving the F-score that appear to lead to confusion. I also settle a question raised at the conference by reconstructing an account from Van Rijsbergen's classic text Information Retrieval.

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14

Christie, Christina A. "Advancing Empirical Scholarship to Further Develop Evaluation Theory and Practice." Canadian Journal of Program Evaluation 26, no. 1 (2011): 1–18. http://dx.doi.org/10.3138/cjpe.026.001.

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Abstract: Good theory development is grounded in empirical inquiry. In the context of educational evaluation, the development of empirically grounded theory has important benefits for the field and the practitioner. In particular, a shift to empirically derived theory will assist in advancing more systematic and contextually relevant evaluation practice, as well as lead to the development of contingency theories that specify the conditions under which particular evaluation practices are optimal. In contrast to the historical outside-in stance, empirical research on the field of evaluation must

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15

Koutsabasis, Panayiotis. "Empirical Evaluations of Interactive Systems in Cultural Heritage." International Journal of Computational Methods in Heritage Science 1, no. 1 (2017): 100–122. http://dx.doi.org/10.4018/ijcmhs.2017010107.

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An increasing number of interactive systems aim to enhance the user experience (UX) of visitors at museums, archaeological places and cultural sites. This paper presents a review of empirical evaluations of interactive systems in cultural heritage (CH) based on a systematically selected sample of 53 publications from 2012-2016. Empirical evaluations examine the degree an interactive system satisfies user goals and expectations and are inherently complex activities that require careful planning and selection of methods. The review reports on (a) interactive systems of CH in terms of: purpose, t

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16

Patiño, David, and Francisco Gómez-García. "Do Quasi-Hyperbolic Preferences Explain Academic Procrastination? An Empirical Evaluation." Revista Hacienda Pública Española 230, no. 3 (2019): 95–124. http://dx.doi.org/10.7866/hpe-rpe.19.3.4.

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17

Saranya, R. Kamala, and Dr S. Duraisamy Dr. S. Duraisamy. "An Empirical Analysis on Training & Development Evaluation Across Organizations." International Journal of Scientific Research 3, no. 4 (2012): 262–63. http://dx.doi.org/10.15373/22778179/apr2014/91.

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18

Lozano Povis, Arlitt Amy, and Elías Adrián Sanabria Perez. "Experimental Evaluation of an Empirical Equation in a Gaseous Flow." Chemistry & Chemical Technology 18, no. 1 (2024): 57–65. http://dx.doi.org/10.23939/chcht18.01.057.

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In this paper, the estimation error of Dr. Pole's empirical equation was evaluated using copper pipes of different diameters (0.00953, 0.0127, 0.01588 m), under different flow pressure conditions (0, 300, 500, 1000, 1500, 2000, 2500, 3000 L/h). To carry out the experiments, the following instruments were used: an air compressor, 2 flow valves, a needle valve, a gas rotameter, copper piping, pressure gauges and transmitters, a Norus data logger with 4 to 20 mA output signals, thermocouples, and thermoresistors. They allow us to establish that the air pressure drops when the flowing through the

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19

Naveed, Sidra, Gunnar Stevens, and Dean Robin-Kern. "An Overview of the Empirical Evaluation of Explainable AI (XAI): A Comprehensive Guideline for User-Centered Evaluation in XAI." Applied Sciences 14, no. 23 (2024): 11288. https://doi.org/10.3390/app142311288.

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Recent advances in technology have propelled Artificial Intelligence (AI) into a crucial role in everyday life, enhancing human performance through sophisticated models and algorithms. However, the focus on predictive accuracy has often resulted in opaque black-box models that lack transparency in decision-making. To address this issue, significant efforts have been made to develop explainable AI (XAI) systems that make outcomes comprehensible to users. Various approaches, including new concepts, models, and user interfaces, aim to improve explainability, build user trust, enhance satisfaction

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20

Zugal, Stefan, Cornelia Haisjackl, Jakob Pinggera, and Barbara Weber. "Empirical Evaluation of Test Driven Modeling." International Journal of Information System Modeling and Design 4, no. 2 (2013): 23–43. http://dx.doi.org/10.4018/jismd.2013040102.

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Declarative approaches to process modeling are regarded well suited for highly volatile environments as they provide a high degree of flexibility. However, problems in understanding and maintaining declarative process models impede their usage. To compensate for these shortcomings, Test Driven Modeling (TDM) has been proposed. This paper reports on an empirical investigation in which TDM is viewed from two different angles. First, the impact of TDM on communication is explored in a case study. Results indicate that domain experts are inclined to use test cases for communicating with the model

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21

Fetais, Noora, and Peter Cheng. "Empirical evaluation of constraint diagrams notation." Qatar Foundation Annual Research Forum Proceedings, no. 2012 (October 2012): CSPS13. http://dx.doi.org/10.5339/qfarf.2012.csps13.

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22

Handzic, Meliha, and Joon Ho-Hur. "Corporate E-Learning: An Empirical Evaluation." Journal of Information & Knowledge Management 04, no. 04 (2005): 229–35. http://dx.doi.org/10.1142/s0219649205001195.

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This paper explores user views of the current e-learning practices in an organisational setting. Eighty employees of Korean Air, who were enrolled in an e-learning course, participated in the study on a voluntary basis. Data on employees' perceptions of importance and satisfaction with their course portal were gathered by administering a survey questionnaire. The study revealed that employees considered all portal features as quite important and satisfying, although some more than others. They also agreed that there was scope for further improvement through adding extra functionality. These fi

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23

Hedbring, Charles. "Empirical Software Evaluation: A Practical Alternative." Australian Journal of Human Communication Disorders 15, no. 2 (1987): 75–88. http://dx.doi.org/10.3109/asl2.1987.15.issue-2.06.

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24

Selby, R. W., V. R. Basili, and F. T. Baker. "Cleanroom Software Development: An Empirical Evaluation." IEEE Transactions on Software Engineering SE-13, no. 9 (1987): 1027–37. http://dx.doi.org/10.1109/tse.1987.233525.

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25

Phillips, P. J., and K. W. Bowyer. "Empirical Evaluation of Computer Vision Algorithms." IEEE Transactions on Pattern Analysis and Machine Intelligence 21, no. 4 (1999): 289–90. http://dx.doi.org/10.1109/tpami.1999.761260.

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26

Hao Wu, Aswin C. Sankaranarayanan, and Rama Chellappa. "Online Empirical Evaluation of Tracking Algorithms." IEEE Transactions on Pattern Analysis and Machine Intelligence 32, no. 8 (2010): 1443–58. http://dx.doi.org/10.1109/tpami.2009.135.

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27

Thoemmes, Felix. "Empirical evaluation of directional-dependence tests." International Journal of Behavioral Development 39, no. 6 (2015): 560–69. http://dx.doi.org/10.1177/0165025415582055.

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Testing of directional dependence is a method to infer causal direction that recently has attracted some attention. Previous examples by e.g. von Eye and DeShon (2012a) and extensive simulation studies by Pornprasertmanit and Little (2012) have demonstrated that under specific assumptions, directional-dependence tests can recover the true causal direction between two variables. Simulation results are important in the evaluation of any statistical method, but they are necessarily less complex than real data that come with potential irregularities (e.g. departures from linearity, presence of con

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28

WEISNER, CONSTANCE. "INNOVATIVE POLICY STRATEGIES NEED EMPIRICAL EVALUATION." Addiction 106, no. 12 (2011): 2069–70. http://dx.doi.org/10.1111/j.1360-0443.2011.03562.x.

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29

Cheatham, James M. "The Empirical Evaluation of Clinical Practice:." Journal of Social Service Research 10, no. 2-4 (1987): 163–77. http://dx.doi.org/10.1300/j079v10n02_10.

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30

Cousins, J. Bradley, and Kenneth A. Leithwood. "Current Empirical Research on Evaluation Utilization." Review of Educational Research 56, no. 3 (1986): 331–64. http://dx.doi.org/10.3102/00346543056003331.

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This paper reviews empirical research conducted during the past 15 years on the use of evaluation results. Sixty-five studies in education, mental health, and social services are described in terms of their methodological characteristics, their orientation toward dependent and independent variables, and the relationships between such variables. A conceptual framework is developed that lists 12 factors that influence use; six of these factors are associated with characteristics of evaluation implementation and six with characteristics of decision or policy setting. The factors are discussed in

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31

Sherman, Douglas J. "Empirical Evaluation of Longshore-Current Models." Geographical Review 78, no. 2 (1988): 158. http://dx.doi.org/10.2307/214174.

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32

Wang, Zhemin. "An Empirical Evaluation Of Goodwill Accounting." Journal of Applied Business Research (JABR) 9, no. 4 (2011): 127. http://dx.doi.org/10.19030/jabr.v9i4.6003.

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While the current amortization period for goodwill is criticized by many financial statement preparers for being too short, the accounting profession and regulatory bodies indicate the intention of further reducing the amortization period. This study documents consistent evidence suggesting that any attempt to significantly shorten the amortization period may impose an unfair penalty on firms with goodwill assets and cause the reported goodwill to be significantly understated.

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33

Feng, Wei, Miguel Figliozzi, and Robert L. Bertini. "Empirical Evaluation of Transit Signal Priority." Transportation Research Record: Journal of the Transportation Research Board 2488, no. 1 (2015): 20–31. http://dx.doi.org/10.3141/2488-03.

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34

Offutt, A. J., and S. D. Lee. "An empirical evaluation of weak mutation." IEEE Transactions on Software Engineering 20, no. 5 (1994): 337–44. http://dx.doi.org/10.1109/32.286422.

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35

Kadipasaoglu, Sükran N., Julio L. Peixoto, and Basheer M. Khumawala. "Global manufacturing practices: an empirical evaluation." Industrial Management & Data Systems 99, no. 3 (1999): 101–8. http://dx.doi.org/10.1108/02635579910370652.

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36

Pospíšil, Leopold. "Empirical Evaluation of Theories of Peasantry." Lidé města 10, no. 2 (2008): 192–203. https://doi.org/10.14712/12128112.3716.

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Like other social sciences, anthropology has theorized about peasantry and its basic concepts in many ways, most of which contradict each other. Shanin viewed them as fitting four major categories. First, the European authors have presented peasantry as an earlier cultural tradition which lags behind modern socio-economic development. For Marx, peasantry meant a class of producers formerly exploited by elite of the pre-capitalist society, which presently represents a leftover from the preceding evolutionary societal stage (1975: 3). Authors of the third category, like Chayanov, regarded peasan

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37

Whitaker, Stephan, and Thomas J. Fitzpatrick. "LAND BANK 2.0: AN EMPIRICAL EVALUATION." Journal of Regional Science 56, no. 1 (2015): 156–75. http://dx.doi.org/10.1111/jors.12206.

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38

Arpaci, Remzi H., David E. Culler, Arvind Krishnamurthy, Steve G. Steinberg, and Katherine Yelick. "Empirical evaluation of the CRAY-T3D." ACM SIGARCH Computer Architecture News 23, no. 2 (1995): 320–31. http://dx.doi.org/10.1145/225830.224443.

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39

Hafner, A. N. "Empirical Testing: The Prototypers Evaluation Mechanism." Naval Engineers Journal 109, no. 1 (1997): 41–46. http://dx.doi.org/10.1111/j.1559-3584.1997.tb01929.x.

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40

Fernández-Garcı́a, N. L., R. Medina-Carnicer, A. Carmona-Poyato, F. J. Madrid-Cuevas, and M. Prieto-Villegas. "Characterization of empirical discrepancy evaluation measures." Pattern Recognition Letters 25, no. 1 (2004): 35–47. http://dx.doi.org/10.1016/j.patrec.2003.08.011.

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41

McCarthy, Philip M., and Scott Jarvis. "vocd: A theoretical and empirical evaluation." Language Testing 24, no. 4 (2007): 459–88. http://dx.doi.org/10.1177/0265532207080767.

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42

Boyle, Craig, Swee Hor Teh, and Clay Williams. "An Empirical Evaluation of Hypertext Interfaces." Hypermedia 2, no. 3 (1990): 235–48. http://dx.doi.org/10.1080/09558543.1990.12031185.

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43

Hanks, Brian. "Empirical evaluation of distributed pair programming." International Journal of Human-Computer Studies 66, no. 7 (2008): 530–44. http://dx.doi.org/10.1016/j.ijhcs.2007.10.003.

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44

García-Peñalvo, Francisco José, Lourdes Moreno López, and Mª Cruz Sánchez-Gómez. "Empirical evaluation of educational interactive systems." Quality & Quantity 52, no. 6 (2018): 2427–34. http://dx.doi.org/10.1007/s11135-018-0808-4.

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45

Sawada, Yasuyuki. "The immiserizing growth: an empirical evaluation." Applied Economics 41, no. 13 (2009): 1613–20. http://dx.doi.org/10.1080/00036840802052099.

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46

Rozell, Elizabeth J., Charles E. Pettijohn, and R. Stephen Parker. "An empirical evaluation of emotional intelligence." Journal of Management Development 21, no. 4 (2002): 272–89. http://dx.doi.org/10.1108/02621710210430272.

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47

Pauley, Wayne. "Cloud Provider Transparency: An Empirical Evaluation." IEEE Security & Privacy Magazine 8, no. 6 (2010): 32–39. http://dx.doi.org/10.1109/msp.2010.140.

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48

Manegold, Stefan. "An empirical evaluation of XQuery processors." Information Systems 33, no. 2 (2008): 203–20. http://dx.doi.org/10.1016/j.is.2007.05.004.

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49

Khari, Manju, and Prabhat Kumar. "Empirical Evaluation of Hill Climbing Algorithm." International Journal of Applied Metaheuristic Computing 8, no. 4 (2017): 27–40. http://dx.doi.org/10.4018/ijamc.2017100102.

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The software is growing in size and complexity every day due to which strong need is felt by the research community to search for the techniques which can optimize test cases effectively. The current study is inspired by the collective behavior of finding paths from the colony of food and uses different versions of Hill Climbing Algorithm (HCA) such as Stochastic, and Steepest Ascent HCA for the purpose of finding a good optimal solution. The performance of the proposed algorithm is verified on the basis of three parameters comprising of optimized test cases, time is taken during the optimizat

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50

Cleland, Timothy P., Kristyn Voegele, and Mary H. Schweitzer. "Empirical Evaluation of Bone Extraction Protocols." PLoS ONE 7, no. 2 (2012): e31443. http://dx.doi.org/10.1371/journal.pone.0031443.

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