Les algorithmes génétiques - JM Alliot. Algorithme génétique. Un article de Wikipédia, l'encyclopédie libre.
Origines[modifier | modifier le code] La popularisation des algorithmes génétiques sera l'œuvre de David Goldberg à travers son livre Genetic Algorithms in Search, Optimization, and Machine Learning (1989). Ce livre est encore édité aujourd'hui. En Europe, la première conférence sur ce type de sujet fut l'European Conference on Artificial Life en 1991 (elle a fêté ses 20 ans en 2011), coorganisée par Francisco Varela et Paul Bourgine. [1001.1889] Cheating for Problem Solving: A Genetic Algorithm with Social Interactions. Kevin Slavin: How algorithms shape our world. » A Speculative Post on the Idea of Algorithmic Authority Clay Shirky. Jack Balkin invited me to be on a panel yesterday at Yale’s Information Society Project conference, Journalism & The New Media Ecology, and I used my remarks to observe that one of the things up for grabs in the current news environment is the nature of authority.
In particular, I noted that people trust new classes of aggregators and filters, whether Google or Twitter or Wikipedia (in its ‘breaking news’ mode.) I called this tendency algorithmic authority. I hadn’t used that phrase before yesterday, so it’s not well worked out (and I didn’t coin it — as Jeff Jarvis noted at the time, Google lists a hundred or so previous occurrences.)
Algorithmique. Optimisation et algorithmes génétiques - MAGNIN.plil.net. The Algorithmic Origins of Life - Sara Walker (SETI Talks) Laboratory for Web Algorithmics. Click here for PhD projects in I. Some of the CAutoD principles are discussed in: Other relevant papers can also be downloaded in pdf: Book 1: Parallel Processing in a Control Systems Environment, E Rogers & Y Li, Prentice Hall Series on Systems and Control Engineering, 1993, 364 pp, ISBN 0-13-651530-4.
Book 2: Real-World Applications of Evolutionary Computing, S Cagnoni, R Poli, & Y Li, et al, Springer-Verlag Lecture Notes in Computer Science, 2000, 396 pp, Volume 1803/2000, Berlin, ISSN 0302-9743, ISBN 978-3-540-67353-8, DOI 10.1007/3-540-45561-2. See also Evolutionary algorithms in engineering applications, D Dasgupta, Z Michalewicz, eds., Springer.
More publications on computational intelligence and applications may be downloaded via ePrints library hereor via repositories below: A smart-object recognition algorithm that doesn’t need humans. (Credit: BYU Photo) BYU engineer Dah-Jye Lee has created an algorithm that can accurately identify objects in images or video sequences — without human calibration.
“In most cases, people are in charge of deciding what features to focus on and they then write the algorithm based off that,” said Lee, a professor of electrical and computer engineering. “With our algorithm, we give it a set of images and let the computer decide which features are important.” Humans need not apply Not only is Lee’s genetic algorithm able to set its own parameters, but it also doesn’t need to be reset each time a new object is to be recognized — it learns them on its own. Lee likens the idea to teaching a child the difference between dogs and cats. Can Algorithms Find the Best Intelligence Analysts? The U.S intelligence community has a long history of blowing big calls — the fall of the Berlin Wall, Saddam’s WMD, 9/11.
But in each collective fail, there were individual analysts who got it right. Now, the spy agencies want a better way to sort the accurate from the unsound, by applying principles of mathematics to weigh and rank the input of different experts. Network Science. Complex systems made simple. Albert-László Barabási and Yang-Yu Liu, together with their collaborator Jean-Jacques Slotine at M.I.T., have developed a method for observing large, complex systems.
In the image above, red dots represent sensor nodes, which are required to reconstruct the entire internal state of one such system. Image by Mauro Martino. Pinterest’s Founder: Algorithms Don’t Know What You Want. In 2012 the startup Pinterest became a peer of more established social sites by offering things that they didn’t—an attractive design, a focus on images rather than text, and a mostly female population of users.
On Pinterest, people use virtual “pinboards” to curate collections of images related to their hobbies and interests, discovering new items for their virtual hoards on the boards of friends and in the site’s personalized recommendations. Tom Simonite, MIT Technology Review’s senior IT editor, recently spoke with Ben Silbermann, Pinterest’s cofounder and CEO, about the company’s popularity. What was the need you were trying to fill when you created Pinterest? We started making Pinterest around 2009, when there was a lot of attention being paid to social services that were focused on real-time text-based feeds like Facebook and Twitter. We felt the things that we enjoyed doing in the real world were hard to express in that format. The site is established and popular. The Rise of the Algorithmic Medium. The 10 Algorithms That Dominate Our World.
A clustering algorithm based on swarm intelligence. This paper focuses on swarm intelligence based clustering algorithm.
A clustering algorithm based on swarm intelligence is systematically proposed. It derived from a basic model interpreting ant colony organization of cemeteries. Some important concepts, such as swarm similarity, swarm similarity coefficient and probability conversion function are also proposed. A simplified probability conversion function is given for simplifying adaptation of parameters, meanwhile the importance of swarm similarity coefficient for the algorithm is analyzed. The Top 10 Algorithms in Data Mining. Trust Is Not An Algorithm: Big Data Are Hot, But They Also Miss A Lot. [Illustration from By some accounts the world’s information is doubling every two years.
Dictionary of Algorithms and Data Structures. This web site is hosted by the Software and Systems Division, Information Technology Laboratory, NIST in collaboration with the FASTAR group.
Development of this dictionary started in 1998 under the editorship of Paul E. Black. This is a dictionary of algorithms, algorithmic techniques, data structures, archetypal problems, and related definitions. Algorithms include common functions, such as Ackermann's function. Problems include traveling salesman and Byzantine generals. Don't use this site to cheat. List of algorithms. The following is a list of algorithms along with one-line descriptions for each. Combinatorial algorithms General combinatorial algorithms Graph algorithms Graph drawing Network theory Routing for graphs Algorithme à estimation de distribution. Un article de Wikipédia, l'encyclopédie libre. Les algorithmes à estimation de distribution résolvent des problèmes d'optimisation en échantillonnant un modèle de distribution, dont les paramètres évoluent via des opérateurs de sélection.
National Institute of Standards and Technology. It defines a large number of terms relating to algorithms and data structures. For algorithms and data structures not necessarily mentioned here, see list of algorithms and list of data structures. This list of terms was originally derived from the index of that document, and is in the public domain, as it was compiled by a Federal Government employee as part of a Federal Government work. Contourner les algorithmes. La lecture de la semaine nous vient de The Atlantic et du toujours pertinent Alexis Madrigal (@alexismadrigal), le titre de son article “Contre l’idée que les algorithmes sont objectifs”. “Quand un résultat provient d’un ordinateur sur la base de statistiques, cela doit être objectif, non ? Pas de biais possible, à la différence de notre jugement, nous Homo Sapiens défectueux.
Inférence bayésienne. Un article de Wikipédia, l'encyclopédie libre. Le raisonnement bayésien s'intéresse aux cas où une proposition pourrait être vraie ou fausse, non pas en raison de son rapport logique à des axiomes tenus pour assurément vrais, mais selon des observations où subsiste une incertitude. Moteur d'inférence. Un article de Wikipédia, l'encyclopédie libre.
Un moteur d'inférence (du verbe « inférer » qui signifie « déduire ») est un logiciel correspondant à un algorithme de simulation des raisonnements déductifs. Un moteur d'inférence permet aux systèmes experts de conduire des raisonnements logiques et de dériver des conclusions à partir d'une base de faits et d'une base de connaissances. Bayesian Network - AI Depot. Statistical inference. In statistics, statistical inference is the process of drawing conclusions from data that are subject to random variation, for example, observational errors or sampling variation. Initial requirements of such a system of procedures for inference and induction are that the system should produce reasonable answers when applied to well-defined situations and that it should be general enough to be applied across a range of situations.
Inferential statistics are used to test hypotheses and make estimations using sample data. The outcome of statistical inference may be an answer to the question "what should be done next? ", where this might be a decision about making further experiments or surveys, or about drawing a conclusion before implementing some organizational or governmental policy. Introduction Scope The Algorithm: Idiom of Modern Science. By Bernard Chazelle hen the great Dane of 20th century physics, Niels Bohr, was not busy chewing on a juicy morsel of quantum mechanics, he was known to yap away witticisms worthy of Yogi Berra. The classic Bohrism “Prediction is difficult, especially about the future” alas came too late to save Lord Kelvin.
Just as physics was set to debut in Einstein's own production of Extreme Makeover, Kelvin judged the time ripe to pen the field's obituary: “There is nothing new to be discovered in physics now.” Not his lordship's finest hour. Nor his worst. Gloat not at a genius' misfortunes. Human Workers, Managed by an Algorithm. Global workforce: Remote digital workers earned $0.32 each for producing these self-portraits.
Stephanie Hamilton is part of something larger than herself. Cours DEA J-M Fouet. How Algorithms and Editors Can Work Together to Burst the "Filter Bubble" The algorithms that surface content for us on Facebook and Google are miracles of modern programming. But Eli Pariser, author and chairman of the board at MoveOn.org, has concerns. In March, Pariser gave a popular TED talk about "filter bubbles" — the idea that when search and social networks only serve us content that we "like," we're not seeing content we need.
The Human Algorithm: Redefining the Value of Data. InShare246 The onslaught of real-time social, local, mobile (SoLoMo) technology is nothing short of overwhelming. Your Life is an Algorithm, Your Brain is an Operating System. Meet the algorithm that can learn “everything about anything” Filtrage bayésien du spam. TinkerPop. Ken's Home Page. Algorithm Distinguishes Memes from Ordinary Information — The Physics arXiv Blog. Bayesia - Prenez les bonnes décisions.
About EPlex - Evolutionary Complexity Research Group at UCF. Learning Webs. Algorithmic culture. “Culture now has two audiences: people and machines” — Futurists’ Views. Clever Algorithms: Nature-Inspired Programming Recipes. Introduction to Algorithms. Bayes pour les nuls. Table Row Attributes 6. Algorithms bookmarks. BayesiaLab & Bayesian Networks. Portail:Algorithmique. Algorithm. Qu’est-ce qu’un algorithme ? La Clé du Médium Algorithmique. In the Olympics of Algorithms, a Russian Keeps Winning Gold. Algorithms. HyperNEAT User's Page. Algorithmia - Open Marketplace for Algorithms.