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• Counting viable sublist lengths from an array.

  - by Ben B.

  This is for a genetic algorithm fitness function, so it is important I can do this as efficiently as possible, as it will be repeated over and over. Lets say there is a function foo(int[] array) that returns true if the array is a "good" array and false if the array is a "bad" array. What makes it good or bad does not matter here. Given the full array [1,6,8,9,5,11,45,16,9], lets say that subarray [1,6,8] is a "good" array and [9,5,11,45] is a "good" array. Furthermore [5,11,45,16,9] is a "good" array, and also the longest "good" subarray. Notice that while [9,5,11,45] is a "good" array, and [5,11,45,16,9] is a "good" array, [9,5,11,45,16,9] is a "bad" array. We wants the length counts of all "good" arrays, but not subarrays of "good" arrays. Furthermore, as described above, a "good" array might begin in the middle of another "good" array, but the combination of the two might be a "bad" array.

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• GA written in Java

  - by EnderMB

  I am attempting to write a Genetic Algorithm based on techniques I had picked up from the book "AI Techniques for Game Programmers" that uses a binary encoding and fitness proportionate selection (also known as roulette wheel selection) on the genes of the population that are randomly generated within the program in a two-dimensional array. I recently came across a piece of pseudocode and have tried to implement it, but have come across some problems with the specifics of what I need to be doing. I've checked a number of books and some open-source code and am still struggling to progress. I understand that I have to get the sum of the total fitness of the population, pick a random number between the sum and zero, then if the number is greater than the parents to overwrite it, but I am struggling with the implementation of these ideas. Any help in the implementation of these ideas would be very much appreciated as my Java is rusty.

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• method for specialized pathfinding?

  - by rlbond

  I am working on a roguelike in my (very little) free time. Each level will basically be a few rectangular rooms connected together by paths. I want the paths between rooms to be natural-looking and windy, however. For example, I would not consider the following natural-looking: B X X X XX XX XX AXX I really want something more like this: B X XXXX X X X X AXXXXXXXX These paths must satisfy a few properties: I must be able to specify an area inside which they are bounded, I must be able to parameterize how windy and lengthy they are, The lines should not look like they started at one path and ended at the other. For example, the first example above looks as if it started at A and ended at B, because it basically changed directions repeatedly until it lined up with B and then just went straight there. I was hoping to use A*, but honestly I have no idea what my heuristic would be. I have also considered using a genetic algorithm, but I don't know how practical that method might end up. My question is, what is a good way to get the results I want? Please do not just specify a method like "A*" or "Dijkstra's algorithm," because I also need help with a good heuristic.

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• Using Traveling Salesman Solver to Decide Hamiltonian Path

  - by Firas Assaad

  This is for a project where I'm asked to implement a heuristic for the traveling salesman optimization problem and also the Hamiltonian path or cycle decision problem. I don't need help with the implementation itself, but have a question on the direction I'm going in. I already have a TSP heuristic based on a genetic algorithm: it assumes a complete graph, starts with a set of random solutions as a population, and works to improve the population for a number of generations. Can I also use it to solve the Hamiltonian path or cycle problems? Instead of optimizing to get the shortest path, I just want to check if there is a path. Now any complete graph will have a Hamiltonian path in it, so the TSP heuristic would have to be extended to any graph. This could be done by setting the edges to some infinity value if there is no path between two cities, and returning the first path that is a valid Hamiltonian path. Is that the right way to approach it? Or should I use a different heuristic for Hamiltonian path? My main concern is whether it's a viable approach since I can be somewhat sure that TSP optimization works (because you start with solutions and improve them) but not if a Hamiltonian path decider would find any path in a fixed number of generations. I assume the best approach would be to test it myself, but I'm constrained by time and thought I'd ask before going down this route... (I could find a different heuristic for Hamiltonian path instead)

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• Special scheduling Algorithm (pattern expansion)

  - by tovare

  Question Do you think genetic algorithms worth trying out for the problem below, or will I hit local-minima issues? I think maybe aspects of the problem is great for a generator / fitness-function style setup. (If you've botched a similar project I would love hear from you, and not do something similar) Thank you for any tips on how to structure things and nail this right. The problem I'm searching a good scheduling algorithm to use for the following real-world problem. I have a sequence with 15 slots like this (The digits may vary from 0 to 20) : 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 (And there are in total 10 different sequences of this type) Each sequence needs to expand into an array, where each slot can take 1 position. 1 1 0 0 1 1 1 0 0 0 1 1 1 0 0 1 1 0 0 1 1 1 0 0 0 1 1 1 0 0 0 0 1 1 0 0 0 1 1 1 0 0 0 1 1 0 0 1 1 0 0 0 1 1 1 0 0 0 1 1 The constraints on the matrix is that: [row-wise, i.e. horizontally] The number of ones placed, must either be 11 or 111 [row-wise] The distance between two sequences of 1 needs to be a minimum of 00 The sum of each column should match the original array. The number of rows in the matrix should be optimized. The array then needs to allocate one of 4 different matrixes, which may have different number of rows: A, B, C, D A, B, C and D are real-world departments. The load needs to be placed reasonably fair during the course of a 10-day period, not to interfere with other department goals. Each of the matrix is compared with expansion of 10 different original sequences so you have: A1, A2, A3, A4, A5, A6, A7, A8, A9, A10 B1, B2, B3, B4, B5, B6, B7, B8, B9, B10 C1, C2, C3, C4, C5, C6, C7, C8, C9, C10 D1, D2, D3, D4, D5, D6, D7, D8, D9, D10 Certain spots on these may be reserved (Not sure if I should make it just reserved/not reserved or function-based). The reserved spots might be meetings and other events The sum of each row (for instance all the A's) should be approximately the same within 2%. i.e. sum(A1 through A10) should be approximately the same as (B1 through B10) etc. The number of rows can vary, so you have for instance: A1: 5 rows A2: 5 rows A3: 1 row, where that single row could for instance be: 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 etc.. Sub problem* I'de be very happy to solve only part of the problem. For instance being able to input: 1 1 2 3 4 2 2 3 4 2 2 3 3 2 3 And get an appropriate array of sequences with 1's and 0's minimized on the number of rows following th constraints above.

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• How to choose an integer linear programming solver ?

  - by Cassie

  Hi all, I am newbie for integer linear programming. I plan to use a integer linear programming solver to solve my combinatorial optimization problem. I am more familiar with C++/object oriented programming on an IDE. Now I am using NetBeans with Cygwin to write my applications most of time. May I ask if there is an easy use ILP solver for me? Or it depends on the problem I want to solve ? I am trying to do some resources mapping optimization. Please let me know if any further information is required. Thank you very much, Cassie.

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• Mutation Problem - Clojure

  - by Silanglaya Valerio

  having trouble changing an element of my function represented as a list. code for random function: (defn makerandomtree-10 [pc maxdepth maxwidth fpx ppx] (if-let [output (if (and (< (rand) fpx) (> maxdepth 0)) (let [head (nth operations (rand-int (count operations))) children (doall (loop[function (list) width maxwidth] (if (pos? width) (recur (concat function (list (makerandomtree-10 pc (dec maxdepth) (+ 2 (rand-int (- maxwidth 1))) fpx ppx))) (dec width)) function)))] (concat (list head) children)) (if (and (< (rand) ppx) (>= pc 0)) (nth parameters (rand-int (count parameters))) (rand-int 100)))] output )) I will provide also a mutation function, which is still not good enough. I need to be able to eval my statement, so the following is still insufficient. (defn mutate-5 "chooses a node changes that" [function pc maxwidth pchange] (if (< (rand) pchange) (let [output (makerandomtree-10 pc 3 maxwidth 0.5 0.6)] (if (seq? output) output (list output))) ;mutate the children of root ;declare an empty accumulator list, with root as its head (let [head (list (first function)) children (loop [acc(list) walker (next function)] (println "----------") (println walker) (println "-----ACC-----") (println acc) (if (not walker) acc (if (or (seq? (first function)) (contains? (set operations) (first function))) (recur (concat acc (mutate-5 walker pc maxwidth pchange)) (next walker)) (if (< (rand) pchange) (if (some (set parameters) walker) (recur (concat acc (list (nth parameters (rand-int (count parameters))))) (if (seq? walker) (next walker) nil)) (recur (concat acc (list (rand-int 100))) (if (seq? walker) (next walker) nil))) (recur acc (if (seq? walker) (next walker) nil)))) ))] (concat head (list children))))) (side note: do you have any links/books for learning clojure?)

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• Roulette wheel selection algorithm

  - by swati

  Can anyone provide some pseudo code for a roulette selection function? How would I implement this: I don't really understand how to read this math notation.I want General algorithm to this.

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• How to choose a integer linear programming solver ?

  - by Cassie

  Hi all, I am newbie for integer linear programming. I plan to use a integer linear programming solver to solve my combinational optimization problem. I am more familiar with C++/object oriented programming on an IDE. Now I am using NetBeans with Cygwin to write my applications most of time. May I ask if there is an easy use ILP solver for me? Or does it depend on the problem I want to solve ? I am trying to do some resources mapping optimization. please let me know if any further information is required. Thank you very much, Cassie.

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• Looking for evolutionary music example code

  - by Dan Dyer

  I would like to implement an interactive evolutionary algorithm for generating music (probably just simple melodies to start with). I'd like to use JFugue for this. Its website claims that it is well-suited to evolutionary music, but I can't find any evolutionary examples. I already have a framework to provide the evolutonary machinery. What I am looking for is some simple, working code that demonstrates viable approaches for the musical part (e.g. suitable encodings and evolutionary operators for the evolved tunes). I have some ideas how it might be achieved, but I'm not particularly knowledgeable about music theory, so to start with I'd like to just reimplement something that is known to work. So does anybody have, or know of, any freely available code (any language is fine) that demonstrates one or more approaches to evolutionary music? EDIT: I'm specifically looking for evolutionary code rather than other techniques that could be used for music synthesis.

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• "Undefined symbols" linker error with simple template class

  - by intregus

  Been away from C++ for a few years and am getting a linker error from the following code: Gene.h #ifndef GENE_H_INCLUDED #define GENE_H_INCLUDED template <typename T> class Gene { public: T getValue(); void setValue(T value); void setRange(T min, T max); private: T value; T minValue; T maxValue; }; #endif // GENE_H_INCLUDED Gene.cpp #include "Gene.h" template <typename T> T Gene<T>::getValue() { return this->value; } template <typename T> void Gene<T>::setValue(T value) { if(value >= this->minValue && value <= this->minValue) { this->value = value; } } template <typename T> void Gene<T>::setRange(T min, T max) { this->minValue = min; this->maxValue = max; } Using Code::Blocks and GCC if it matters to anyone. Also, clearly porting some GA stuff to C++ for fun and practice.

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• What is the optimum way to select the most dissimilar individuals from a population?

  - by Aaron D

  I have tried to use k-means clustering to select the most diverse markers in my population, for example, if we want to select 100 lines I cluster the whole population to 100 clusters then select the closest marker to the centroid from each cluster. The problem with my solution is it takes too much time (probably my function needs optimization), especially when the number of markers exceeds 100000. So, I will appreciate it so much if anyone can show me a new way to select markers that maximize diversity in my population and/or help me optimize my function to make it work faster. Thank you # example: library(BLR) data(wheat) dim(X) mdf<-mostdiff(t(X), 100,1,nstart=1000) Here is the mostdiff function that i used: mostdiff <- function(markers, nClust, nMrkPerClust, nstart=1000) { transposedMarkers <- as.array(markers) mrkClust <- kmeans(transposedMarkers, nClust, nstart=nstart) save(mrkClust, file="markerCluster.Rdata") # within clusters, pick the markers that are closest to the cluster centroid # turn the vector of which markers belong to which clusters into a list nClust long # each element of the list is a vector of the markers in that cluster clustersToList <- function(nClust, clusters) { vecOfCluster <- function(whichClust, clusters) { return(which(whichClust == clusters)) } return(apply(as.array(1:nClust), 1, vecOfCluster, clusters)) } pickCloseToCenter <- function(vecOfCluster, whichClust, transposedMarkers, centers, pickHowMany) { clustSize <- length(vecOfCluster) # if there are fewer than three markers, the center is equally distant from all so don't bother if (clustSize < 3) return(vecOfCluster[1:min(pickHowMany, clustSize)]) # figure out the distance (squared) between each marker in the cluster and the cluster center distToCenter <- function(marker, center){ diff <- center - marker return(sum(diff*diff)) } dists <- apply(transposedMarkers[vecOfCluster,], 1, distToCenter, center=centers[whichClust,]) return(vecOfCluster[order(dists)[1:min(pickHowMany, clustSize)]]) } }

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• Using c#,c/c++ or java to improve BBN with GA

  - by madicemickael

  I have a little problem in my little project , I wish that someone here could help me! I am planning to use a bayesian network as a decision factor in my game AI and I want to improve the decision making every step of the way , anyone knows how to do that ? Any tutorials / existing implementations will be very good,I hope some of you could help me. I heard that a programmer in this community did a good implementation of this put together for poker game AI.I am planning to use it like him ,but in another poker(Texas) or maybe Rentz. Looking for C/c++ or c# or java code. Thanks , Mike

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• algorithm to combine data for linear fit?

  - by BoldlyBold

  I'm not sure if this is the best place to ask this, but you guys have been helpful with plenty of my CS homework in the past so I figure I'll give it a shot. I'm looking for an algorithm to blindly combine several dependent variables into an index that produces the best linear fit with an external variable. Basically, it would combine the dependent variables using different mathematical operators, include or not include each one, etc. until an index is developed that best correlates with my external variable. Has anyone seen/heard of something like this before? Even if you could point me in the right direction or to the right place to ask, I would appreciate it. Thanks.

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• So are we ever getting the technological singularity

  - by jsoldi

  I´m still waiting for an AI robot that will pass the Turing test. I keep going back to http://www.a-i.com/ and nothing. I don´t know much about AI but, did anyone ever tried to make a genetic algorithm whose evolution algorithm itself evolves? Or how about one whose algorithm that makes the genetic algorithm evolve, evolves? Or one whose genetic algorithm that makes the genetic algorithm that makes the genetic algorithm evolve, evolves? Or how about an algorithm that abstracts all this into a potentially infinitely deep tree of genetic evolution algorithms? Aren´t we just failing as programmers? And I don´t think we can blame the processors speed. If you make and application that simulates consciousness you will get a Nobel prize no matter how many hours it takes to respond to your questions. But nobody did it. It almost reminds me to Randi´s $1000000 paranormal challenge. As I keep going back to AI chat bots, they keep getting better at changing the subject on a way that seems natural. But if I tell them something like "if 'x' is 2 then whats two times 'x'?" then they don't have a clue what I'm talking about. And I don't think they need a whole human brain simulation to be able to answer to something like that. They don't need feelings or perception. This is just language and logics. I don't think my perception of the color red gives me the ability to understand that if 'x' is 2 then two times 'x' is 4. I'm sure we are just missing some elemental principle we cannot grasp because it's probably stuck behind our eyes. What do you think?

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• Is it wise to store a big lump of json on a database row

  - by Ieyasu Sawada

  I have this project which stores product details from amazon into the database. Just to give you an idea on how big it is: [{"title":"Genetic Engineering (Opposing Viewpoints)","short_title":"Genetic Engineering ...","brand":"","condition":"","sales_rank":"7171426","binding":"Book","item_detail_url":"http://localhost/wordpress/product/?asin=0737705124","node_list":"Books > Science & Math > Biological Sciences > Biotechnology","node_category":"Books","subcat":"","model_number":"","item_url":"http://localhost/wordpress/wp-content/ecom-plugin-redirects/ecom_redirector.php?id=128","details_url":"http://localhost/wordpress/product/?asin=0737705124","large_image":"http://localhost/wordpress/wp-content/plugins/ecom/img/large-notfound.png","medium_image":"http://localhost/wordpress/wp-content/plugins/ecom/img/medium-notfound.png","small_image":"http://localhost/wordpress/wp-content/plugins/ecom/img/small-notfound.png","thumbnail_image":"http://localhost/wordpress/wp-content/plugins/ecom/img/thumbnail-notfound.png","tiny_img":"http://localhost/wordpress/wp-content/plugins/ecom/img/tiny-notfound.png","swatch_img":"http://localhost/wordpress/wp-content/plugins/ecom/img/swatch-notfound.png","total_images":"6","amount":"33.70","currency":"$","long_currency":"USD","price":"$33.70","price_type":"List Price","show_price_type":"0","stars_url":"","product_review":"","rating":"","yellow_star_class":"","white_star_class":"","rating_text":" of 5","reviews_url":"","review_label":"","reviews_label":"Read all ","review_count":"","create_review_url":"http://localhost/wordpress/wp-content/ecom-plugin-redirects/ecom_redirector.php?id=132","create_review_label":"Write a review","buy_url":"http://localhost/wordpress/wp-content/ecom-plugin-redirects/ecom_redirector.php?id=19186","add_to_cart_action":"http://localhost/wordpress/wp-content/ecom-plugin-redirects/add_to_cart.php","asin":"0737705124","status":"Only 7 left in stock.","snippet_condition":"in_stock","status_class":"ninstck","customer_images":["http://localhost/wordpress/wp-content/uploads/2013/10/ecom_images/51M2vvFvs2BL.jpg","http://localhost/wordpress/wp-content/uploads/2013/10/ecom_images/31FIM-YIUrL.jpg","http://localhost/wordpress/wp-content/uploads/2013/10/ecom_images/51M2vvFvs2BL.jpg","http://localhost/wordpress/wp-content/uploads/2013/10/ecom_images/51M2vvFvs2BL.jpg"],"disclaimer":"","item_attributes":[{"attr":"Author","value":"Greenhaven Press"},{"attr":"Binding","value":"Hardcover"},{"attr":"EAN","value":"9780737705126"},{"attr":"Edition","value":"1"},{"attr":"ISBN","value":"0737705124"},{"attr":"Label","value":"Greenhaven Press"},{"attr":"Manufacturer","value":"Greenhaven Press"},{"attr":"NumberOfItems","value":"1"},{"attr":"NumberOfPages","value":"224"},{"attr":"ProductGroup","value":"Book"},{"attr":"ProductTypeName","value":"ABIS_BOOK"},{"attr":"PublicationDate","value":"2000-06"},{"attr":"Publisher","value":"Greenhaven Press"},{"attr":"SKU","value":"G0737705124I2N00"},{"attr":"Studio","value":"Greenhaven Press"},{"attr":"Title","value":"Genetic Engineering (Opposing Viewpoints)"}],"customer_review_url":"http://localhost/wordpress/wp-content/ecom-customer-reviews/0737705124.html","flickr_results":["http://localhost/wordpress/wp-content/uploads/2013/10/ecom_images/5105560852_06c7d06f14_m.jpg"],"freebase_text":"No around the web data available yet","freebase_image":"http://localhost/wordpress/wp-content/plugins/ecom/img/freebase-notfound.jpg","ebay_related_items":[{"title":"Genetic Engineering (Introducing Issues With Opposing Viewpoints), , Good Book","image":"http://localhost/wordpress/wp-content/uploads/2013/10/ecom_images/140.jpg","url":"http://localhost/wordpress/wp-content/ecom-plugin-redirects/ecom_redirector.php?id=12165","currency_id":"$","current_price":"26.2"},{"title":"Genetic Engineering Opposing Viewpoints by DAVID BENDER - 1964 Hardcover","image":"http://localhost/wordpress/wp-content/uploads/2013/10/ecom_images/140.jpg","url":"http://localhost/wordpress/wp-content/ecom-plugin-redirects/ecom_redirector.php?id=130","currency_id":"AUD","current_price":"11.99"}],"no_follow":"rel=\"nofollow\"","new_tab":"target=\"_blank\"","related_products":[],"super_saver_shipping":"","shipping_availability":"","total_offers":"7","added_to_cart":""}] So the structure for the table is: asin title details (the product details in json) Will the performance suffer if I have to store like 10,000 products? Is there any other way of doing this? I'm thinking of the following, but the current setup is really the most convenient one since I also have to use the data on the client side: store the product details in a file. So something like ASIN123.json store the product details in one big file. (I'm guessing it will be a drag to extract data from this file) store each of the fields in the details in its own table field Thanks in advance!

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• Attributes of an Ethical Programmer?

  - by ahmed

  Software that we write has ramifications in the real world. If not, it wouldn't be very useful. Thus, it has the potential to sweep across the world faster than a deadly manmade virus or to affect society every bit as much as genetic manipulation. Maybe we can't see how right now, but in the future our code will have ever-greater potential for harm or good. Of course, there's the issue of hacking. That's clearly a crime. Or is it that clear? Isn't hacking acceptable for our government in the event of national security? What about for other governments? Cases of life-and-death emergency? Tracking down deadbeat parents? Screening the genetic profile of job candidates? Where is the line drawn? Who decides? Do programmers have responsibility for how their code is used? What if a programmer writes code to pry into confidential information or copy-protected material? Does he bear responsibility along with the person who used the program? What about a programmer who knowingly or unknowingly writes code to "fix the books?" Should he be liable?

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• How to generate and encode (for use in GA), random, strict, binary rooted trees with N leaves?

  - by Peter Simon

  First, I am an engineer, not a computer scientist, so I apologize in advance for any misuse of nomenclature and general ignorance of CS background. Here is the motivational background for my question: I am contemplating writing a genetic algorithm optimizer to aid in designing a power divider network (also called a beam forming network, or BFN for short). The BFN is intended to distribute power to each of N radiating elements in an array of antennas. The fraction of the total input power to be delivered to each radiating element has been specified. Topologically speaking, a BFN is a strictly binary, rooted tree. Each of the (N-1) interior nodes of the tree represents the input port of an unequal, binary power splitter. The N leaves of the tree are the power divider outputs. Given a particular power divider topology, one is still free to map the power divider outputs to the array inputs in an arbitrary order. There are N! such permutations of the outputs. There are several considerations in choosing the desired ordering: 1) The power ratio for each binary coupler should be within a specified range of values. 2) The ordering should be chosen to simplify the mechanical routing of the transmission lines connecting the power divider. The number of ouputs N of the BFN may range from, say, 6 to 22. I have already written a genetic algorithm optimizer that, given a particular BFN topology and desired array input power distribution, will search through the N! permutations of the BFN outputs to generate a design with compliant power ratios and good mechanical routing. I would now like to generalize my program to automatically generate and search through the space of possible BFN topologies. As I understand it, for N outputs (leaves of the binary tree), there are $C_{N-1}$ different topologies that can be constructed, where $C_N$ is the Catalan number. I would like to know how to encode an arbitrary tree having N leaves in a way that is consistent with a chromosomal description for use in a genetic algorithm. Also associated with this is the need to generate random instances for filling the initial population, and to implement crossover and mutations operators for this type of chromosome. Any suggestions will be welcome. Please minimize the amount of CS lingo in your reply, since I am not likely to be acquainted with it. Thanks in advance, Peter

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• How can I chose the depth of a quadtree?

  - by Evpok

  In a 2d world, using a quadtree to prune pairs in collision detection, how can I chose the depth of said quadtree? The world I am dealing with is mostly made of moving objects¹, so the cost of dispatching the objects between the quadtree cells matter. So what I am interested in is the balance between the gain from less collision checking and the loss from more dispatching. 1. To be completely explicit, autonomous self-replicating cells competing for food sources, in an attempt to show my pupils predator-prey dynamics and genetic evolution at work

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• Best way to implement an AI for Dominion? [on hold]

  - by j will

  I'm creating a desktop client and server backend for the game, Dominion, by Donald X. Vaccarino. I've been reading up on AI techniques and algorithms and I just wanted to what is the best way to implement an AI for such a game? Would it better to look at neural networks, genetic algorithms, decision trees, fuzzy logic, or any other methodology? For those who do not know how Dominion works, check out this part of the wikipedia article: http://en.wikipedia.org/wiki/Dominion_(card_game)#Gameplay

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• SQL Server 2008: Comparing similar records - Need to still display an ID for a record when the JOIN has no matches

  - by aleppke

  I'm writing a SQL Server 2008 report that will compare genetic test results for animals. A genetic test consists of an animalId, a gene and a result. Not all animals will have the same genes tested but I need to be able to display the results side-by-side for a given set of animals and only include the genes that are present for at least one of the selected animals. My TestResult table has the following data in it: animalId gene result 1 a CC 1 b CT 1 d TT 2 a CT 2 b CT 2 c TT 3 a CT 3 b TT 3 c CC 3 d CC 3 e TT I need to generate a result set that looks like the following. Note that Animal 3 is not being displayed (user doesn't want to see its results) and neither are results for Gene "e" since neither Animal 1 nor Animal 2 have a result for that gene: SireID SireResult CalfID CalfResult Gene 1 CC 2 CT a 1 CT 2 CT b 1 NULL 2 TT c 1 TT 2 NULL d But I can only manage to get this: SireID SireResult CalfID CalfResult Gene 1 CC 2 CT a 1 CT 2 CT b NULL NULL 2 TT c 1 TT NULL NULL d This is the query I'm using. SELECT sire.animalId AS 'SireID' ,sire.result AS 'SireResult' ,calf.animalId AS 'CalfID' ,calf.result AS 'CalfResult' ,sire.gene AS 'Gene' FROM (SELECT s.animalId ,s.result ,m1.gene FROM (SELECT [animalId ] ,result ,gene FROM TestResult WHERE animalId IN (1)) s FULL JOIN (SELECT DISTINCT gene FROM TestResult WHERE animalId IN (1, 2)) m1 ON s.marker = m1.marker) sire FULL JOIN (SELECT c.animalId ,c.result ,m2.gene FROM (SELECT animalId ,result ,gene FROM TestResult WHERE animalId IN (2)) c FULL JOIN (SELECT DISTINCT gene FROM TestResult WHERE animalId IN (1, 2)) m2 ON c.gene = m2.gene) calf ON sire.gene = calf.gene How do I get the SireIDs and CalfIDs to display their values when they don't have a record associated with a particular Gene? I was thinking of using COALESCE but I can't figure out how to specify the correct animalId to pass in. Any help would be appreciated.

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• I need to speed up a function. Should I use cython, ctypes, or something else?

  - by Peter Stewart

  I'm having a lot of fun learning Python by writing a genetic programming type of application. I've had some great advice from Torsten Marek, Paul Hankin and Alex Martelli on this site. The program has 4 main functions: generate (randomly) an expression tree. evaluate the fitness of the tree crossbreed mutate As all of generate, crossbreed and mutate call 'evaluate the fitness'. it is the busiest function and is the primary bottleneck speedwise. As is the nature of genetic algorithms, it has to search an immense solution space so the faster the better. I want to speed up each of these functions. I'll start with the fitness evaluator. My question is what is the best way to do this. I've been looking into cython, ctypes and 'linking and embedding'. They are all new to me and quite beyond me at the moment but I look forward to learning one and eventually all of them. The 'fitness function' needs to compare the value of the expression tree to the value of the target expression. So it will consist of a postfix evaluator which will read the tree in a postfix order. I have all the code in python. I need advice on which I should learn and use now: cython, ctypes or linking and embedding. Thank you.

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• trying to switch beginning of an array with another

  - by user1874574

  I have a problem where i am trying to swap two arrays so that they switch beginnings. example: array 1 = (1,2,3,4,5,6,7,8) and array 2 = (11,12,13,14,15,16,17,18) i want to end up with the first array being (11,12,13,14,5,6,7,8) and i want the second array to be (1,2,3,4,15,16,17,18) but for some reason i end up with 1=(11,12,13,14,5,6,7,8) and 2=(11,12,13,14,15,16,17,18) my code is provided below, what am i doing wrong? public static void Mutate(Genetic lowest, Genetic secondLowest) { int halfway = (lowest.getPopulation().length)/2; int[] one = lowest.getPopulation(); int[] two = secondLowest.getPopulation(); int[] temp = secondLowest.getPopulation(); int[] temp2 = lowest.getPopulation(); for(int i = 0; i < halfway; i++){ temp[i] = one[i]; } lowest.setPopulation(temp); for(int i = 0; i < lowest.getPopulation().length; i++){ System.out.print(temp[i]); } System.out.println(); for(int i = 0; i < halfway; i++){ temp2[i] = two[i]; } for(int i = 0; i < lowest.getPopulation().length; i++){ System.out.print(temp2[i]); } }

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• AI Game Programming : Bayesian Networks, how to make efficient?

  - by Mahbubur R Aaman

  We know that AI is one of the most important part of Game Programming. Bayesian networks is one of the core part of AI at Game Programming. Bayesian networks are graphs that compactly represent the relationship between random variables for a given problem. These graphs aid in performing reasoning or decision making in the face of uncertainty. Here me, utilizing the monte carlo method and genetic algorithms. But tooks much time and sometimes crashes due to memory. Is there any way to implement efficiently?

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• Is chess-like AI really inapplicable in turn-based strategy games?

  - by Joh

  Obviously, trying to apply the min-max algorithm on the complete tree of moves works only for small games (I apologize to all chess enthusiasts, by "small" I do not mean "simplistic"). For typical turn-based strategy games where the board is often wider than 100 tiles and all pieces in a side can move simultaneously, the min-max algorithm is inapplicable. I was wondering if a partial min-max algorithm which limits itself to N board configurations at each depth couldn't be good enough? Using a genetic algorithm, it might be possible to find a number of board configurations that are good wrt to the evaluation function. Hopefully, these configurations might also be good wrt to long-term goals. I would be surprised if this hasn't been thought of before and tried. Has it? How does it work?

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