Bizarre Soft Robots Evolve to Run. Genetic Programming: Evolution of Mona Lisa | Roger Alsing Weblog. [EDIT] Added FAQ here: Gallery here: This weekend I decided to play around a bit with genetic programming and put evolution to the test, the test of fine art :-) I created a small program that keeps a string of DNA for polygon rendering. The procedure of the program is quite simple: 0) Setup a random DNA string (application start) 1) Copy the current DNA sequence and mutate it slightly 2) Use the new DNA to render polygons onto a canvas 3) Compare the canvas to the source image 4) If the new painting looks more like the source image than the previous painting did, then overwrite the current DNA with the new DNA 5) repeat from 1 Now to the interesting part :-) Could you paint a replica of the Mona Lisa using only 50 semi transparent polygons? That is the challenge I decided to put my application up to.
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Operational transformation. Operational transformation (OT) is a technology for supporting a range of collaboration functionalities in advanced collaborative software systems. OT was originally invented for consistency maintenance and concurrency control in collaborative editing of plain text documents. Two decades of research has extended its capabilities and expanded its applications to include group undo, locking, conflict resolution, operation notification and compression, group-awareness, HTML/XML and tree-structured document editing, collaborative office productivity tools, application-sharing, and collaborative computer-aided media design tools (see OTFAQ).
In 2009 OT was adopted as a core technique behind the collaboration features in Apache Wave and Google Docs. History[edit] Operational Transformation was pioneered by C. Ellis and S. Gibbs[1] in the GROVE (GRoup Outline Viewing Edit) system in 1989. System architecture[edit] Basics[edit] generated by two users at collaborating sites 1 and 2, respectively. Why Bloom filters work the way they do. Imagine you’re a programmer who is developing a new web browser. There are many malicious sites on the web, and you want your browser to warn users when they attempt to access dangerous sites. For example, suppose the user attempts to access You’d like a way of checking whether domain is known to be a malicious site. What’s a good way of doing this? An obvious naive way is for your browser to maintain a list or set data structure containing all known malicious domains. In this post I’ll describe a data structure which provides an excellent way of solving this kind of problem.
Most explanations of Bloom filters cut to the chase, quickly explaining the detailed mechanics of how Bloom filters work. In this post I take an unusual approach to explaining Bloom filters. Of course, this means that if your goal is just to understand the mechanics of Bloom filters, then this post isn’t for you. A stylistic note: Most of my posts are code-oriented. Of objects. Is a member of . ? Bloom filter. Bloom proposed the technique for applications where the amount of source data would require an impracticably large hash area in memory if "conventional" error-free hashing techniques were applied. He gave the example of a hyphenation algorithm for a dictionary of 500,000 words, out of which 90% follow simple hyphenation rules, but the remaining 10% require expensive disk accesses to retrieve specific hyphenation patterns. With sufficient core memory, an error-free hash could be used to eliminate all unnecessary disk accesses; on the other hand, with limited core memory, Bloom's technique uses a smaller hash area but still eliminates most unnecessary accesses.
For example, a hash area only 15% of the size needed by an ideal error-free hash still eliminates 85% of the disk accesses (Bloom (1970)). More generally, fewer than 10 bits per element are required for a 1% false positive probability, independent of the size or number of elements in the set (Bonomi et al. (2006)). . . .
As before. Bitap algorithm. The bitap algorithm (also known as the shift-or, shift-and or Baeza-Yates–Gonnet algorithm) is an approximate string matching algorithm. The algorithm tells whether a given text contains a substring which is "approximately equal" to a given pattern, where approximate equality is defined in terms of Levenshtein distance — if the substring and pattern are within a given distance k of each other, then the algorithm considers them equal. The algorithm begins by precomputing a set of bitmasks containing one bit for each element of the pattern.
Then it is able to do most of the work with bitwise operations, which are extremely fast. Due to the data structures required by the algorithm, it performs best on patterns less than a constant length (typically the word length of the machine in question), and also prefers inputs over a small alphabet. Exact searching[edit] The bitap algorithm for exact string searching, in full generality, looks like this in pseudocode: Fuzzy searching[edit] Hash Functions. Engineering Puzzles.