Search results for 'Algorithmes' (try it on Scholar)

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  1. Jean Largeault (1978). Matérialisme Dialectique et Logique. Par Pierre Raymond. Collection Algorithmes. Paris, Maspéro, 1977. 182 p. Dialogue 17 (03):562-566.score: 15.0
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  2. Carl G. Hempel (1950). Review: Michel Cazin, Algorithmes et Theories Physiques; Michel Cazin, Algorithme et Construction d'une Theorie Unifiante. [REVIEW] Journal of Symbolic Logic 14 (4):254-255.score: 15.0
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  3. Hugues Leblanc (1964). Review: B. A. Trahtenbrot, A. Chauvin, Algorithmes et Machines a Calculer. [REVIEW] Journal of Symbolic Logic 29 (3):147-148.score: 15.0
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  4. Gene F. Rose (1959). Review: Jean Porte, Systemes de Post, Algorithmes de Markov. [REVIEW] Journal of Symbolic Logic 24 (3):239-240.score: 15.0
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  5. Gene F. Rose (1958). Review: M. S. Leavitt, Algebras de Boole E Analise de Circuitos; Jacques Riguet, Sur les Rapports Entre les Concepts de Machine de Multipole Et de Structure Algebrique; Jacques Riguet, Algorithmes de Markov Et Theorie des Machines. [REVIEW] Journal of Symbolic Logic 23 (1):62-62.score: 15.0
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  6. Matthias Müller-Hannemann & Stefan Schirra (eds.) (2010). Algorithm Engineering: Bridging the Gap Between Algorithm Theory and Practice. Springer.score: 12.0
    Driven by concrete applications, Algorithm Engineering complements theory by the benefits of experimentation and puts equal emphasis on all aspects arising during a cyclic solution process ranging from realistic modeling, design, analysis, ...
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  7. Marcin Miłkowski (2009). Is Evolution Algorithmic? Minds and Machines 19 (4):465-475.score: 8.0
    In Darwin’s Dangerous Idea, Daniel Dennett claims that evolution is algorithmic. On Dennett’s analysis, evolutionary processes are trivially algorithmic because he assumes that all natural processes are algorithmic. I will argue that there are more robust ways to understand algorithmic processes that make the claim that evolution is algorithmic empirical and not conceptual. While laws of nature can be seen as compression algorithms of information about the world, it does not follow logically that they are implemented as algorithms by physical (...)
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  8. Paola Cantu' & Italo Testa (2011). Algorithms and Arguments: The Foundational Role of the ATAI-Question. In Frans H. van Eemeren, Bart Garssen, David Godden & Gordon Mitchell (eds.), Proceedings of the Seventh International Conference of the International Society for the Study of Argumentation (pp. 192-203). Rozenberg / Sic Sat.score: 8.0
    Argumentation theory underwent a significant development in the Fifties and Sixties: its revival is usually connected to Perelman's criticism of formal logic and the development of informal logic. Interestingly enough it was during this period that Artificial Intelligence was developed, which defended the following thesis (from now on referred to as the AI-thesis): human reasoning can be emulated by machines. The paper suggests a reconstruction of the opposition between formal and informal logic as a move against a premise of an (...)
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  9. Carol E. Cleland (2001). Recipes, Algorithms, and Programs. Minds and Machines 11 (2):219-237.score: 8.0
    In the technical literature of computer science, the concept of an effective procedure is closely associated with the notion of an instruction that precisely specifies an action. Turing machine instructions are held up as providing paragons of instructions that "precisely describe" or "well define" the actions they prescribe. Numerical algorithms and computer programs are judged effective just insofar as they are thought to be translatable into Turing machine programs. Nontechnical procedures (e.g., recipes, methods) are summarily dismissed as ineffective on the (...)
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  10. Murray Clarke (1996). Darwinian Algorithms and Indexical Representation. Philosophy of Science 63 (1):27-48.score: 8.0
    In this paper, I argue that accurate indexical representations have been crucial for the survival and reproduction of homo sapiens sapiens. Specifically, I want to suggest that reliable processes have been selected for because of their indirect, but close, connection to true belief during the Pleistocene hunter-gatherer period of our ancestral history. True beliefs are not heritable, reliable processes are heritable. Those reliable processes connected with reasoning take the form of Darwinian Algorithms: a plethora of specialized, domain-specific inference rules designed (...)
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  11. Katja de Vries (2010). Identity, Profiling Algorithms and a World of Ambient Intelligence. Ethics and Information Technology 12 (1):71-85.score: 8.0
    The tendency towards an increasing integration of the informational web into our daily physical world (in particular in so-called Ambient Intelligent technologies which combine ideas derived from the field of Ubiquitous Computing, Intelligent User Interfaces and Ubiquitous Communication) is likely to make the development of successful profiling and personalization algorithms, like the ones currently used by internet companies such as Amazon , even more important than it is today. I argue that the way in which we experience ourselves necessarily goes (...)
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  12. Wayne Aitken & Jeffrey A. Barrett (2008). Abstraction in Algorithmic Logic. Journal of Philosophical Logic 37 (1):23 - 43.score: 8.0
    We develop a functional abstraction principle for the type-free algorithmic logic introduced in our earlier work. Our approach is based on the standard combinators but is supplemented by the novel use of evaluation trees. Then we show that the abstraction principle leads to a Curry fixed point, a statement C that asserts C ⇒ A where A is any given statement. When A is false, such a C yields a paradoxical situation. As discussed in our earlier work, this situation leaves (...)
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  13. Wayne Aitken & Jeffrey A. Barrett (2007). Stability and Paradox in Algorithmic Logic. Journal of Philosophical Logic 36 (1):61 - 95.score: 8.0
    There is significant interest in type-free systems that allow flexible self-application. Such systems are of interest in property theory, natural language semantics, the theory of truth, theoretical computer science, the theory of classes, and category theory. While there are a variety of proposed type-free systems, there is a particularly natural type-free system that we believe is prototypical: the logic of recursive algorithms. Algorithmic logic is the study of basic statements concerning algorithms and the algorithmic rules of inference between such statements. (...)
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  14. Martin Peterson (2011). Is There an Ethics of Algorithms? Ethics and Information Technology 13 (3):251-260.score: 8.0
    We argue that some algorithms are value-laden, and that two or more persons who accept different value-judgments may have a rational reason to design such algorithms differently. We exemplify our claim by discussing a set of algorithms used in medical image analysis: In these algorithms it is often necessary to set certain thresholds for whether e.g. a cell should count as diseased or not, and the chosen threshold will partly depend on the software designer’s preference between avoiding false positives and (...)
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  15. Rolf Niedermeier (2006). Invitation to Fixed-Parameter Algorithms. Oxford University Press.score: 8.0
    A fixed-parameter is an algorithm that provides an optimal solution to a combinatorial problem. This research-level text is an application-oriented introduction to the growing and highly topical area of the development and analysis of efficient fixed-parameter algorithms for hard problems. The book is divided into three parts: a broad introduction that provides the general philosophy and motivation; followed by coverage of algorithmic methods developed over the years in fixed-parameter algorithmics forming the core of the book; and a discussion of the (...)
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  16. Kees van Deemter, Albert Gatt, Ielka van der Sluis & Richard Power (2011). Generation of Referring Expressions: Assessing the Incremental Algorithm. Cognitive Science 36 (5):799-836.score: 8.0
    A substantial amount of recent work in natural language generation has focused on the generation of ‘‘one-shot’’ referring expressions whose only aim is to identify a target referent. Dale and Reiter's Incremental Algorithm (IA) is often thought to be the best algorithm for maximizing the similarity to referring expressions produced by people. We test this hypothesis by eliciting referring expressions from human subjects and computing the similarity between the expressions elicited and the ones generated by algorithms. It turns out that (...)
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  17. Jeff Edmonds (2008). How to Think About Algorithms. Cambridge University Press.score: 8.0
    There are many algorithm texts that provide lots of well-polished code and proofs of correctness. Instead, this book presents insights, notations, and analogies to help the novice describe and think about algorithms like an expert. By looking at both the big picture and easy step-by-step methods for developing algorithms, the author helps students avoid the common pitfalls. He stresses paradigms such as loop invariants and recursion to unify a huge range of algorithms into a few meta-algorithms. Part of the goal (...)
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  18. Tim Holmes & Johannes M. Zanker (2013). Investigating Preferences for Colour-Shape Combinations with Gaze Driven Optimization Method Based on Evolutionary Algorithms. Frontiers in Psychology 4:926.score: 8.0
    Studying aesthetic preference is notoriously difficult because it targets individual experience. Eye movements provide a rich source of behavioural measures that directly reflect subjective choice. To determine individual preferences for simple composition rules we here use fixation duration as the fitness measure in a Gaze Driven Evolutionary Algorithm (GDEA), which has been used as a tool to identify aesthetic preferences (Holmes & Zanker, 2012). In the present study, the GDEA was used to investigate the preferred combination of colour and shape (...)
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  19. Luis Rosa (2014). Justification and Cognitive Algorithms. Philosophia 42 (2):503-515.score: 8.0
    In this paper, we offer an alternative interpretation for the claim that ‘S is justified in believing that φ’. First, we present what seems to be a common way of interpreting this claim: as an attribution of propositional justification. According to this interpretation, being justified is just a matter of having confirming evidence. We present a type of case that does not fit well with the standard concept, where considerations about cognition are made relevant. The concept of cognitive algorithm is (...)
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  20. Thomas Bartz-Beielstein (2008). How Experimental Algorithmics Can Benefit From Mayo's Extensions to Neyman–Pearson Theory of Testing. Synthese 163 (3):385 - 396.score: 8.0
    Although theoretical results for several algorithms in many application domains were presented during the last decades, not all algorithms can be analyzed fully theoretically. Experimentation is necessary. The analysis of algorithms should follow the same principles and standards of other empirical sciences. This article focuses on stochastic search algorithms, such as evolutionary algorithms or particle swarm optimization. Stochastic search algorithms tackle hard real-world optimization problems, e.g., problems from chemical engineering, airfoil optimization, or bio-informatics, where classical methods from mathematical optimization fail. (...)
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  21. O. B. Lupanov (ed.) (2005). Stochastic Algorithms: Foundations and Applications: Third International Symposium, Saga 2005, Moscow, Russia, October 20-22, 2005: Proceedings. [REVIEW] Springer.score: 8.0
    This book constitutes the refereed proceedings of the Third International Symposium on Stochastic Algorithms: Foundations and Applications, SAGA 2005, held in Moscow, Russia in October 2005. The 14 revised full papers presented together with 5 invited papers were carefully reviewed and selected for inclusion in the book. The contributed papers included in this volume cover both theoretical as well as applied aspects of stochastic computations whith a special focus on new algorithmic ideas involving stochastic decisions and the design and evaluation (...)
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  22. Emiel Krahmer, Ruud Koolen & Mariët Theune (2012). Is It That Difficult to Find a Good Preference Order for the Incremental Algorithm? Cognitive Science 36 (5):837-841.score: 8.0
    In a recent article published in this journal (van Deemter, Gatt, van der Sluis, & Power, 2012), the authors criticize the Incremental Algorithm (a well-known algorithm for the generation of referring expressions due to Dale & Reiter, 1995, also in this journal) because of its strong reliance on a pre-determined, domain-dependent Preference Order. The authors argue that there are potentially many different Preference Orders that could be considered, while often no evidence is available to determine which is a good one. (...)
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  23. Hector Zenil, The World is Either Algorithmic or Mostly Random.score: 8.0
    I will propose the notion that the universe is digital, not as a claim about what the universe is made of but rather about the way it unfolds. Central to the argument will be the concepts of symmetry breaking and algorithmic probability, which will be used as tools to compare the way patterns are distributed in our world to the way patterns are distributed in a simulated digital one. These concepts will provide a framework for a discussion of the informational (...)
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  24. M. W. Bunder & R. M. Rizkalla (2009). Proof-Finding Algorithms for Classical and Subclassical Propositional Logics. Notre Dame Journal of Formal Logic 50 (3):261-273.score: 8.0
    The formulas-as-types isomorphism tells us that every proof and theorem, in the intuitionistic implicational logic $H_\rightarrow$, corresponds to a lambda term or combinator and its type. The algorithms of Bunder very efficiently find a lambda term inhabitant, if any, of any given type of $H_\rightarrow$ and of many of its subsystems. In most cases the search procedure has a simple bound based roughly on the length of the formula involved. Computer implementations of some of these procedures were done in Dekker. (...)
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  25. Katja Vries (2010). Identity, Profiling Algorithms and a World of Ambient Intelligence. Ethics and Information Technology 12 (1):71-85.score: 8.0
    The tendency towards an increasing integration of the informational web into our daily physical world (in particular in so-called Ambient Intelligent technologies which combine ideas derived from the field of Ubiquitous Computing, Intelligent User Interfaces and Ubiquitous Communication) is likely to make the development of successful profiling and personalization algorithms, like the ones currently used by internet companies such as Amazon, even more important than it is today. I argue that the way in which we experience ourselves necessarily goes through (...)
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  26. Robert Zimmer, Robert Holte & Alan MacDonald (1997). The Impact of Representation on the Efficacy of Artificial Intelligence: The Case of Genetic Algorithms. [REVIEW] AI and Society 11 (1-2):76-87.score: 8.0
    This paper is about representations for Artificial Intelligence systems. All of the results described in it involve engineering the representation to make AI systems more effective. The main AI techniques studied here are varieties of search: path-finding in graphs, and probablilistic searching via simulated annealing and genetic algorithms. The main results are empirical findings about the granularity of representation in implementations of genetic algorithms. We conclude by proposing a new algorithm, called “Long-Term Evolution,” which is a genetic algorithm running on (...)
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  27. Jeffrey Barrett (2007). Stability and Paradox in Algorithmic Logic. Journal of Philosophical Logic 36 (1):61 - 95.score: 8.0
    There is significant interest in type-free systems that allow flexible self-application. Such systems are of interest in property theory, natural language semantics, the theory of truth, theoretical computer science, the theory of classes, and category theory. While there are a variety of proposed type-free systems, there is a particularly natural type-free system that we believe is prototypical: the logic of recursive algorithms. Algorithmic logic is the study of basic statements concerning algorithms and the algorithmic rules of inference between such statements. (...)
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  28. Sean Devine (2014). An Algorithmic Information Theory Challenge to Intelligent Design. Zygon 49 (1):42-65.score: 8.0
    William Dembski claims to have established a decision process to determine when highly unlikely events observed in the natural world are due to Intelligent Design. This article argues that, as no implementable randomness test is superior to a universal Martin-Löf test, this test should be used to replace Dembski's decision process. Furthermore, Dembski's decision process is flawed, as natural explanations are eliminated before chance. Dembski also introduces a fourth law of thermodynamics, his “law of conservation of information,” to argue that (...)
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  29. V. Rybakov (2008). Discrete Linear Temporal Logic with Current Time Point Clusters, Deciding Algorithms. Logic and Logical Philosophy 17 (1-2):143-161.score: 8.0
    The paper studies the logic TL(NBox+-wC) – logic of discrete linear time with current time point clusters. Its language uses modalities Diamond+ (possible in future) and Diamond- (possible in past) and special temporal operations, – Box+w (weakly necessary in future) and Box-w (weakly necessary in past). We proceed by developing an algorithm recognizing theorems of TL(NBox+-wC), so we prove that TL(NBox+-wC) is decidable. The algorithm is based on reduction of formulas to inference rules and converting the rules in special reduced (...)
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  30. Elżbieta Hajnicz (1996). Applying Allen's Constraint Propagation Algorithm for Non-Linear Time. Journal of Logic, Language and Information 5 (2):157-175.score: 8.0
    The famous Allen's interval relations constraint propagation algorithm was intended for linear time. Its 13 primitive relations define all the possible mutual locations of two intervals on the time-axis. In this paper an application of the algorithm for non-linear time is suggested. First, a new primitive relation is added. It is called excludes since an occurrence of one event in a certain course of events excludes an occurrence of the other event in this course. Next, new composition rules for relations (...)
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  31. Kees van Deemter, Albert Gatt, Ielka van der Sluis & Richard Power (2012). Assessing the Incremental Algorithm: A Response to Krahmer Et Al. Cognitive Science 36 (5):842-845.score: 8.0
    This response discusses the experiment reported in Krahmer et al.’s Letter to the Editor of Cognitive Science. We observe that their results do not tell us whether the Incremental Algorithm is better or worse than its competitors, and we speculate about implications for reference in complex domains, and for learning from ‘‘normal” (i.e., non-semantically-balanced) corpora.
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  32. Engin Bozdag (2013). Bias in Algorithmic Filtering and Personalization. Ethics and Information Technology 15 (3):209-227.score: 8.0
    Online information intermediaries such as Facebook and Google are slowly replacing traditional media channels thereby partly becoming the gatekeepers of our society. To deal with the growing amount of information on the social web and the burden it brings on the average user, these gatekeepers recently started to introduce personalization features, algorithms that filter information per individual. In this paper we show that these online services that filter information are not merely algorithms. Humans not only affect the design of the (...)
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  33. Bruno Mesz, Mariano Sigman & Marcos Trevisan (2012). A Composition Algorithm Based on Crossmodal Taste-Music Correspondences. Frontiers in Human Neuroscience 6.score: 8.0
    While there is broad consensus about the structural similarities between language and music, comparably less attention has been devoted to semantic correspondences between these two ubiquitous manifestations of human culture. We have investigated the relations between music and a narrow and bounded domain of semantics: the words and concepts referring to taste sensations. In a recent work, we found that taste words were consistently mapped to musical parameters. Bitter is associated with low-pitched and continuous music (legato), salty is characterized by (...)
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  34. Yunfu Shen (1999). Elimination of Algorithmic Quantifiers for Ordered Differential Algebra. Archive for Mathematical Logic 38 (3):139-144.score: 8.0
    In [2], Singer proved that the theory of ordered differential fields has a model completion, i.e, the theory of closed ordered differential fields, CODF. As a result, CODF admits elimination of quantifiers. In this paper we give an algorithm to eliminate the quantifiers of CODF-formulas.
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  35. Susan G. Sterrett (2002). Nested Algorithms and the Original Imitation Game Test: A Reply to James Moor. [REVIEW] Minds and Machines 12 (1):131-136.score: 7.0
    In "The Status and Future of the Turing Test" (Moor, 2001), which appeared in an earlier issue of this journal, James Moor remarks on my paper "Turing's Two Tests for Intelligence." In my paper I had claimed that, whatever Turing may or may not have thought, the test described in the opening section of Turing's now legendary 1950 paper "Computing Machinery and Intelligence" is not equivalent to, and in fact is superior to, the test described in a passage that occurs (...)
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  36. F. Masarani & S. S. Gokturk (1989). On the Existence of Fair Matching Algorithms. Theory and Decision 26 (3):305-322.score: 7.0
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  37. M. Y. Kao (ed.) (2007). Encyclopedia of Algorithms. Springer.score: 7.0
    The online edition supplements this index with hyperlinks as well as including internal hyperlinks to related entries in the text, CrossRef citations, and links ...
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  38. Kees van Deemter, Albert Gatt, Ielka van der Sluis & Richard Power (2012). Generation of Referring Expressions: Assessing the Incremental Algorithm. Cognitive Science 36 (5):799-836.score: 7.0
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  39. S. Dan Felsenthal & Moshé Machover (1992). Sequential Voting by Veto: Making the Mueller-Moulin Algorithm More Versatile. Theory and Decision 33 (3):223-240.score: 7.0
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  40. David E. Goldberg, Kumara Sastry & Xavier Llorà (2007). Toward Routine Billion‐Variable Optimization Using Genetic Algorithms. Complexity 12 (3):27-29.score: 7.0
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  41. Sean Devine (2006). The Application of Algorithmic Information Theory to Noisy Patterned Strings. Complexity 12 (2):52-58.score: 7.0
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  42. A. P. Ershov & Donald Ervin Knuth (eds.) (1981). Algorithms in Modern Mathematics and Computer Science: Proceedings, Urgench, Uzbek Ssr, September 16-22, 1979. Springer-Verlag.score: 7.0
  43. Susan Hanekon, Sue Berney, Brenda Morrow, George Ntoumenopoulos, Jennifer Paratz, Shane Patman & Quinette Louw (2011). The Validation of a Clinical Algorithm for the Prevention and Management of Pulmonary Dysfunction in Intubated Adults: A Synthesis of Evidence and Expert Opinion. Journal of Evaluation in Clinical Practice 17 (4):801-810.score: 7.0
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  44. Peter Juslin & Magnus Persson (2002). PROBabilities From EXemplars (PROBEX): A “Lazy” Algorithm for Probabilistic Inference From Generic Knowledge. Cognitive Science 26 (5):563-607.score: 7.0
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  45. Carl van Walraven & Vivek Goel (2002). The Effect of a Hepatitis Serology Testing Algorithm on Laboratory Utilization. Journal of Evaluation in Clinical Practice 8 (3):327-332.score: 7.0
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  46. Qingfu Zhang (2004). On the Convergence of a Factorized Distribution Algorithm with Truncation Selection. Complexity 9 (4):17-23.score: 7.0
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  47. S. I. Adi͡an (ed.) (1977). Mathematical Logic, the Theory of Algorithms, and the Theory of Sets. American Mathematical Society.score: 7.0
  48. Ljiljana Brankovic, Yuqing Lin & Bill Smyth (eds.) (2008). Proceedings of the International Workshop on Combinatorial Algorithms, 2007. College Publications.score: 7.0
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  49. Freddy Bugge Christiansen & Marcus W. Feldman (1998). Algorithms, Genetics, and Populations: The Schemata Theorem Revisited. Complexity 3 (3):57-64.score: 7.0
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  50. Kees van Deemter, Albert Gatt, Ielka van der Sluis & Richard Power (2012). Assessing the Incremental Algorithm: A Response to Krahmer Et Al. Cognitive Science 36 (5):842-845.score: 7.0
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