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[Automated Trader Magazine Bootstrap Articles RSS News Feed] Online | Pretests for genetic-programming evolved trading programs ...: In actual fact, in the literature, the results of applying GP for market-timing decisions are typically not very convincing, but the investigators always suggest the possibility of further improvements.If the investigators can first convince that there is something to learn and that GP is suitable for that task, then their conclusion would be less vague and uncertain. We propose here a series of pretests, where GP is tested against a random behav­ior (lottery trading) and against strategies created at random (zero-intelligence strategies), that aim to answer these two crucial questions.

[tt July 2009 Archive] [tt] EC-Digest v23n6: o BIOCOMP'09: Bioinformatics and Computational Biologyo CDES'09: Computer Designo CGVR'09: Computer Graphics and Virtual Realityo CSC'09: Scientific Computingo DMIN'09: Data Miningo EEE'09: e-Learning, e-Business, Enterprise Information Systems, and e-Governmento ERSA'09: Engineering of Reconfigurable Systems and Algorithmso ESA'09: Embedded Systems and Applicationso FCS'09: Foundations of Computer Scienceo FECS'09: Frontiers in Education: Computer Science and Computer Engineeringo GCA'09: Grid Computing and Applicationso GEM'09: Genetic and Evolutionary Methodso ICAI'09: Artificial Intelligenceo ICOMP'09: Internet Computingo ICWN'09: Wireless Networkso IKE'09: Information and Knowledge Engineeringo IPCV'09: Image Processing, Computer Vision, and Pattern Recognitiono MSV'09: Modeling, Simulation and Visualization Methodso PDPTA'09: Parallel and Distributed Processing Techniques and Applicationso SAM'09: Security and Managemento SERP'09: Software Engineering Research and Practiceo SWWS'09: Semantic Web and Web Services

[Automated Trader Magazine Bootstrap Articles RSS News Feed] Pretests for genetic-programming evolved trading programs: 'zero ...: The analysis is illustrated with GP-evolved strategies for three stock exchanges exhibiting different trends. Pretests for genetic-programming evolved trading programs: “zero-intelligence” strategies and lottery trading - Part 1 .

[Irrational Exuberance] Genetic Programming: A Novel Failure @ Irrational Exuberance: This one example is far from sufficient justification, but I'll go a bit further and suggest that genetic programming will only solve problems when you supply a detailed outline of the solution. Genetic algorithms can optimize a solution from supplied components, but genetic programming is inadequate to both discover and optimize a solution.

[Springer Computer Science titles] Genetic Programming Theory and Practice VI: Contributions from the foremost international researchers and practitioners in the GP arena examine the similarities and differences between theoretical and empirical results on real-world problems. The text explores the synergy between theory and practice, producing a comprehensive view of the state of the art in GP application.

[Notional Slurry] Notional Slurry » Search algorithms: No, I’m currently not tackling problems this way (although I would like to), but just making the point that with a growing number of objectives, utilizing convexity is going to be a help, both in getting the algorithms to perform, and in culling the solution set to the most interesting points.

[Roger Alsing Weblog] Genetic Programming: Mona Lisa FAQ « Roger Alsing Weblog: Roger, I would suggest re-running the simulation with a larger population size and some true genetic operators and see what effect that has on the convergence rate. As for the fitness function - I would keep it as is for now as it is unlikely to be a major factor in the convergence rate (at least not as much as the other suggestions).

[Springer Engineering titles] Natural Computing in Computational Finance: Natural Computing in Computational Finance is a innovative volume containing fifteen chapters which illustrate cutting-edge applications of natural computing or agent-based modeling in modern computational finance. Following an introductory chapter the book is organized into three sections.

[Roger Alsing Weblog] Genetic Programming: Evolution of Mona Lisa « Roger Alsing Weblog: If it took about 1M iterations to get to your result with hill climbing, I would expect you could do much much better with a genetic algorithm provided you were smart about how DNA got swapped. If it was swapped spatially, somehow, then you could get a result where a candidate which was good at the face bred with a candidate which was good at the background and you got a candidate which was good at both.

[blog maverick] The Stimulus Plan Update « blog maverick: As is usually the case with any blog post, the vast majority of people don’t actually read the post before they write a response. So the vast majority were dismissed because they did not quality under the required terms.   Of those that did meet the requirements, I gave priority to those that were already operating.  My feeling is that if a business is profitable , or close to it,  they are more likely to know how to use the capital to their advantage than a startup would be.

[Uncommon Descent] John Derbyshire: “I will not do my homework!” | Uncommon Descent: It is fair comment that above, I have cited adequate examples to give a clear enough concept, and that Dembski’s model offers a reasonable filter for deciding at least on clear cases. And, the cases in view are more than clear â┚¬Ã¢â‚¬Å“ they are more or less plain as day: agency is by far and away the best EXPLANATION for the OOL and its macro-level diversity â┚¬Ã¢â‚¬Å“ in the realm of facts we are dealing with inference to best explanation, not demonstrative proofs to an arbitrary standard (which is often â┚¬Ã…“convenientlyâ┚¬Ã‚ substituted when the best explanation does not sit well with one’s worldview;

[Black Hat Announcements] Black Hat USA 2007 Topics and Speakers: He is co-director of the Genetic Algorithms Research and Applications Group or GARAGe. His main interests are genetic algorithms and genetic programming, including theoretical issues (parallel GA/GP) and application issues (design, layout, .

[Uncommon Descent] Chance, Law, Agency or Other? | Uncommon Descent: If, instead, you want to arguw how F2XL got the number on which those calulations are made, that is the necessity of 490 specific mutations to the flagellum, then you ahould discuss F2XL’s previous posts, which detailed that reasoning. Anyway, as you opened the discussion on the calculations themselves (correctly, I would say, because I think F2XL’s calculations were wroong, but still IMO with wrong arguments on yout part), I think you ahould now contribute to that specific part of the discussion, even if you don’t agree on the premises.

[ScienceBlogs Channel : Medicine & Health] Swine flu: the overreaction overreaction : Effect Measure: miso: I was going to do a separate antiviral post and probably will, but it got pushed out by another issue, people to pigs (probably tomorrow afternoon). However in my estimation antivirals are a marginal issue, since they depend on a functioning health services system and an adequate supply and distribution network and they are not that effective.

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