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genetic algorithm in artificial intelligence | genetic algorithm in hindi | Artificial intelligence
 
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Hey friends welcome to well academy here is the topic genetic algorithm in artificial intelligence in hindi DBMS Gate Lectures Full Course FREE Playlist : https://goo.gl/Z7AAyV Facebook Me : https://goo.gl/2zQDpD Click here to subscribe well Academy https://www.youtube.com/wellacademy1 GATE Lectures by Well Academy Facebook Group https://www.facebook.com/groups/1392049960910003/ Thank you for watching share with your friends Follow on : Facebook page : https://www.facebook.com/wellacademy/ Instagram page : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy genetic algorithm in artificial intelligence, genetic algorithm in artificial intelligence in hindi, genetic algorithm in artificial intelligence example, genetic algorithm in artificial intelligence tutorial, genetic algorithm in artificial intelligence in urdu, genetic algorithm in artificial intelligence hindi, genetic algorithm in hindi, genetic algorithm in ai, genetic algorithm artificial intelligence, genetic algorithm, genetic algorithm ai, genetic algorithm well academy, genetic algorithm crossover genetic algorithm tutorial genetic algorithm example genetic algorithm genetic algorithm fitness function genetic algorithm artificial intelligence artificial intelligence well academy well academy artificial intelligence artificial intelligence tutorial artificial intelligence in hindi artificial intelligence lecture artificial intelligence lecture in hindi
Views: 124821 Well Academy
Genetic Algorithms - Georgia Tech - Machine Learning
 
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Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud262/l-521298714/m-534408627 Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud262 Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Views: 14955 Udacity
Introduction to Genetic Algorithms
 
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A brief introduction to genetic algorithms with examples.
Views: 130273 chriskam1250
Genetic Algorithm Part 1 in Hindi
 
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जेनेटिक algo का सरल explanation
Views: 22610 Red Apple Tutorials
Genetic Algorithm | Artificial Intelligence Tutorial in Hindi Urdu | Genetic Algorithm Example
 
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#askfaizan | #syedfaizanahmad PlayList : Artificial Intelligence : https://www.youtube.com/playlist?list=PLhwpdymnbXz4fEjqBoJbvLTIqfZJfXjbH Genetic Algorithm is - Optimization Algorithm - Based on natural phenomenon - Nature inspired approach based on Darwin’s law of Survival of the fittest and  bio-inspired operators such as Pairing Crossover and Mutation. - frequently used to find optimal or near-optimal solutions to difficult problems Optimization- is the process of making something better Terminology - Population - Chromosomes  - Gene Operators are - Selection - Crossover - Mutation for Complete Artificial Intelligence Videos click on the link : https://www.youtube.com/playlist?list=PLhwpdymnbXz4fEjqBoJbvLTIqfZJfXjbH Thank you for watching share with your friends Follow on : Facebook page : https://www.facebook.com/askfaizan1/ Instagram page : https://www.instagram.com/ask_faizan/ Twitter : https://twitter.com/ask_faizan/
Views: 17681 Ask Faizan
Genetic Algorithm
 
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Search based optimization technique.Based on natural selection and natural genetics.
Views: 17055 Smita Tiwari
Genetic Algorithm Example in Artificial Intelligence | Genetic Algorithm in Artificial Intelligence
 
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Hello Friends Welcome to well academy For Notes of Artificial Intelligence click on the link : In this video i have discussed Genetic Algorithm Example in Artificial Intelligence with detail explanation of MAXONE example. as earlier i have explained Genetic Algorithm in Artificial Intelligence. So this video will be properly of Example if you want to see the explanation of Genetic Algorithm in Artificial Intelligence explanation click on below link. Genetic Algorithm in artificial intelligence https://www.youtube.com/watch?v=FwPgHgbncPk&t=2s Facebook Me : https://goo.gl/2zQDpD Click here to subscribe well Academy https://www.youtube.com/wellacademy1 GATE Lectures by Well Academy Facebook Group https://www.facebook.com/groups/1392049960910003/ Thank you for watching share with your friends Follow on : Facebook page : https://www.facebook.com/wellacademy/ Instagram page : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy
Views: 63895 Well Academy
9.1: Genetic Algorithm: Introduction - The Nature of Code
 
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Welcome to part 1 of a new series of videos focused on Evolutionary Computing, and more specifically, Genetic Algorithms. In this tutorial, I introduce the concept of a genetic algorithm, how it can be used to approach "search" problems and how it relates to brute force algorithms. Support this channel on Patreon: https://patreon.com/codingtrain Send me your questions and coding challenges!: https://github.com/CodingTrain/Rainbow-Topics Contact: https://twitter.com/shiffman Links discussed in this video: The Nature of Code: http://natureofcode.com/ BoxCar2D: http://boxcar2d.com/ Source Code for the Video Lessons: https://github.com/CodingTrain/Rainbow-Code p5.js: https://p5js.org/ Processing: https://processing.org For More Genetic Algorithm videos: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6bJM3VgzjNV5YxVxUwzALHV For More Nature of Code videos: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6aFlwukCmDf0-1-uSR7mklK Help us caption & translate this video! http://amara.org/v/Sld6/
Views: 216645 The Coding Train
What is a Genetic Algorithm
 
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Get an introduction to the components of a genetic algorithm. Get a Free MATLAB Trial: https://goo.gl/C2Y9A5 Ready to Buy: https://goo.gl/vsIeA5 Learn more Genetic Algorithms: https://goo.gl/kYxNPo Learn how genetic algorithms are used to solve optimization problems. Examples illustrate important concepts such as selection, crossover, and mutation. Finally, an example problem is solved in MATLAB® using the ga function from Global Optimization Toolbox.
Views: 136829 MATLAB
Genetic Algorithms w/ Java - Tutorial 01
 
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Website + download source code @ http://www.zaneacademy.com | Genetic Algorithms w/ Python - Tutorial 01 @ https://youtu.be/zumC_C0C25c
Views: 27196 zaneacademy
Data Science - Part XIV - Genetic Algorithms
 
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For downloadable versions of these lectures, please go to the following link: http://www.slideshare.net/DerekKane/presentations https://github.com/DerekKane/YouTube-Tutorials This lecture provides an overview on biological evolution and genetic algorithms in a machine learning context. We will start off by going through a broad overview of the biological evolutionary process and then explore how genetic algorithms can be developed that mimic these processes. We will dive into the types of problems that can be solved with genetic algorithms and then we will conclude with a series of practical examples in R which highlights the techniques: The Knapsack Problem, Feature Selection and OLS regression, and constrained optimizations.
Views: 23374 Derek Kane
Genetic Algorithm - explained in 4 minutes
 
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Short introduction to the facts of using genetic algorithms in financial markets. Please review www.whentotrade.com for more details. Watch a GA live in intraday trading: http://youtu.be/JXvJndqOvGc
Views: 158098 whentotrade
Neural Networks in Data Mining | MLP Multi layer Perceptron Algorithm in Data Mining
 
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Classification is a predictive modelling. Classification consists of assigning a class label to a set of unclassified cases Steps of Classification: 1. Model construction: Describing a set of predetermined classes Each tuple/sample is assumed to belong to a predefined class, as determined by the class label attribute. The set of tuples used for model construction is training set. The model is represented as classification rules, decision trees, or mathematical formulae. 2. Model usage: For classifying future or unknown objects Estimate accuracy of the model If the accuracy is acceptable, use the model to classify new data MLP- NN Classification Algorithm The MLP-NN algorithm performs learning on a multilayer feed-forward neural network. It iteratively learns a set of weights for prediction of the class label of tuples. A multilayer feed-forward neural network consists of an input layer, one or more hidden layers, and an output layer. Each layer is made up of units. The inputs to the network correspond to the attributes measured for each training tuple. The inputs are fed simultaneously into the units making up the input layer. These inputs pass through the input layer and are then weighted and fed simultaneously to a second layer of “neuronlike” units, known as a hidden layer. The outputs of the hidden layer units can be input to another hidden layer, and so on. The number of hidden layers is arbitrary, although in practice, usually only one is used. The weighted outputs of the last hidden layer are input to units making up the output layer, which emits the network’s prediction for given tuples. Algorithm of MLP-NN is as follows: Step 1: Initialize input of all weights with small random numbers. Step 2: Calculate the weight sum of the inputs. Step 3: Calculate activation function of all hidden layer. Step 4: Output of all layers For more information and query visit our website: Website : http://www.e2matrix.com Blog : http://www.e2matrix.com/blog/ WordPress : https://teche2matrix.wordpress.com/ Blogger : https://teche2matrix.blogspot.in/ Contact Us : +91 9041262727 Follow Us on Social Media Facebook : https://www.facebook.com/etwomatrix.researchlab Twitter : https://twitter.com/E2MATRIX1 LinkedIn : https://www.linkedin.com/in/e2matrix-training-research Google Plus : https://plus.google.com/u/0/+E2MatrixJalandhar Pinterest : https://in.pinterest.com/e2matrixresearchlab/ Tumblr : https://www.tumblr.com/blog/e2matrix24
Genetic Algorithms - II
 
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Subject: Computer Science Paper: Machine Learning Module: Genetic Algorithms - II Content Writer: Dr.T.V.Geetha
Views: 1154 Vidya-mitra
intro to genetic algorithm part 3 :: Example of GA
 
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This is the part 3 of the series of intro to genetic algorithm tutorials. In this video i have given a mathematical example of Genetic Algorithm. All the key operators of Genetic Algorithm are applied in this example and it is shown that how these operators can help us move towards achieving higher values of the objective Function.
Views: 20503 Ahsan Ashfaq
Genetic Algorithm Explanation
 
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(Summary) Genetic Algorithm: Why? A lot of data has to be analysed and it's not possible to check every possibility. A faster way to find solutions to problems is needed. How? The algorithm is based on evolution in nature. Solutions improve over time using mating and mutation. After some time an almost optimal solution is found. Applications? Design of airplanes, improvement of trading stategies, DNA analysis, simulation of evolution. Created using Adobe After Effects, Adobe Photoshop, Audacity Davids Stepanovs University College London (UCL) Computer Science 2016 ENGS101P: Engineering Challenge 1 - Individual Video Attribution-NonCommercial-ShareAlike 4.0 International
Views: 21130 David Stepanov
Genetic Algorithm For Feature Selection Example
 
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A example of using a genetic algorithm to choose an "optimal" feature subset for simple classification problem. The Code: https://github.com/scoliann/GeneticAlgorithmFeatureSelection
Views: 5743 Stuart C.
13. Learning: Genetic Algorithms
 
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MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston This lecture explores genetic algorithms at a conceptual level. We consider three approaches to how a population evolves towards desirable traits, ending with ranks of both fitness and diversity. We briefly discuss how this space is rich with solutions. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 337151 MIT OpenCourseWare
intro to genetic algorithm part 2
 
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This is the part 2 of the series of intro to genetic algorithm tutorials. In this video i have tried to explain the sophisticated operators of genetic algorithm. The application of GA operators is described in exploring the solution space.
Views: 6995 Ahsan Ashfaq
Mod-01 Lec-38 Genetic Algorithms
 
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Design and Optimization of Energy Systems by Prof. C. Balaji , Department of Mechanical Engineering, IIT Madras. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 157111 nptelhrd
Genetic algorithm full overview in Tamil
 
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Genetic algorithm full overview in Tamil https://youtu.be/jGx4NxIn0J8
Views: 364 Pinky Sriji
Credit Card Fraud Detection System using Genetic Algorithm
 
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Credit Card Fraud Detection System using Genetic Algorithm To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: http://www.jpinfotech.org, Blog: http://www.jpinfotech.blogspot.com Due to the rise and rapid growth of E-Commerce, use of credit cards for online purchases has dramatically increased and it caused an explosion in the credit card fraud. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. In real life, fraudulent transactions are scattered with genuine transactions and simple pattern matching techniques are not often sufficient to detect those frauds accurately. Implementation of efficient fraud detection systems has thus become imperative for all credit card issuing banks to minimize their losses. Many modern techniques based on Artificial Intelligence, Data mining, Fuzzy logic, Machine learning, Sequence Alignment, Genetic Programming etc., has evolved in detecting various credit card fraudulent transactions. A clear understanding on all these approaches will certainly lead to an efficient credit card fraud detection system. This paper presents a survey of various techniques used in credit card fraud detection mechanisms and evaluates each methodology based on certain design criteria.
Views: 1348 JPINFOTECH PROJECTS
SVM optimization using GA & Stydy of Classifiers on Biomedical Databases
 
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This Video covers following 2 tasks Task 1: Study of different classifiers on different Biomedical Databases. Task 2: Optimization of Support Vector Machine using Genetic Algorithm. Task1: Biomedical databases are stroage of biological information. These data sets are used to study the causes of any diseases. These are multi array databases in which hundreds/Thousands of features/attributes of patients are present. There are many Websites for biomedical data sets are available. The UCI Machine Learning Repository is a collection of databases, domain theories, and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms. In this video, following databases are used and their respective source are given below 1. BUPA https://archive.ics.uci.edu/ml/datasets/liver+disorders 2. ILPD https://archive.ics.uci.edu/ml/datasets/ILPD+(Indian+Liver+Patient+Dataset) 3. Diabetes https://archive.ics.uci.edu/ml/datasets/pima+indians+diabetes 4. Original Breast Cancer https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original) 5. Diagnostic Breast Cancer http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29 6. Prognostic Breast Cancer https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Prognostic%29 7. Ovarian Cancer http://home.ccr.cancer.gov/ncifdaproteomics/ppatterns.asp Task 2: It is known in medical science that all features have not the same significance. In this task, we will use the Genetic Algorithm to optimize the classifier (SVM) so that we get the higher accuracy and selected features. Feel free to contact at [email protected] Thank you
Views: 914 Dilip Dubey
Genetic Algorithms Tutorial 07 - data mining + arithmetic operators + JAVA 8
 
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Website + download source code @ http://www.zaneacademy.com
Views: 1099 zaneacademy
Artificial Intelligence (Genetic Algorithm)
 
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Pharo TechTalk given on January 30, 2018
Views: 262 Alexandre Bergel
Genetic Algorithm in SoftComputing and Artificial Intelligence | HINDI
 
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Welcome to TECHNICAL SUPPORT BY RAHUL SAHANI ===================================================== All about technical concepts, technical subject , and mobile application. ------------------------------------------------------------------------------------------------- For more videos please | LIKE , SUBSCRIBE, SHARE, COMMENT |
Operators of Genetic Algorithm in Hindi
 
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Genetic Algorithm me selection , crossover and mutation ke operators
Views: 8016 Red Apple Tutorials
Automatic Time Table Generation Using Genetic Algorithm
 
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Title: Automatic Time Table Generation Using Genetic Algorithm Domain: Data Mining Key Features: 1. Generation of time table using genetic algorithm. 2. Time table generation separately for teacher and students. 3. Downloadable in .xls file 4. Facility of curd model for teacher and students, etc. For more details contact: E-Mail: [email protected] Buy Whole Project Kit for Rs 5000%. Project Kit: • 1 Review PPT • 2nd Review PPT • Full Coding with described algorithm • Video File • Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. Search Terms: 1. 2017 ieee projects 2. latest ieee projects in java 3. latest ieee projects in data mining 4. 2016 – 2017 data mining projects 5. 2016 – 2017 best project center in Chennai 6. best guided ieee project center in Chennai 7. 2016 – 2017 ieee titles 8. 2016 – 2017 base paper 9. 2016 – 2017 java projects in Chennai, Coimbatore, Bangalore, and Mysore 10. time table generation projects 11. instruction detection projects in data mining, network security 12. 2016 – 2017 data mining weka projects 13. 2016 – 2017 b.e projects 14. 2016 – 2017 m.e projects 15. 2016 – 2017 final year projects 16. affordable final year projects 17. latest final year projects 18. best project center in Chennai, Coimbatore, Bangalore, and Mysore 19. 2017 Best ieee project titles 20. best projects in java domain 21. free ieee project in Chennai, Coimbatore, Bangalore, and Mysore 22. 2016 – 2017 ieee base paper free download 23. 2016 – 2017 ieee titles free download 24. best ieee projects in affordable cost 25. ieee projects free download 26. 2017 data mining projects 27. 2017 ieee projects on data mining 28. 2017 final year data mining projects 29. 2017 data mining projects for b.e 30. 2017 data mining projects for m.e 31. 2017 latest data mining projects 32. latest data mining projects 33. latest data mining projects in java 34. data mining projects in weka tool 35. data mining in intrusion detection system 36. intrusion detection system using data mining 37. intrusion detection system using data mining ppt 38. intrusion detection system using data mining technique 39. data mining approaches for intrusion detection 40. data mining in ranking system using weka tool 41. data mining projects using weka 42. data mining in bioinformatics using weka 43. data mining using weka tool 44. data mining tool weka tutorial 45. data mining abstract 46. data mining base paper 47. data mining research papers 2016 - 2017 48. 2016 - 2017 data mining research papers 49. 2017 data mining research papers 50. data mining IEEE Projects 52. data mining and text mining ieee projects 53. 2017 text mining ieee projects 54. text mining ieee projects 55. ieee projects in web mining 56. 2017 web mining projects 57. 2017 web mining ieee projects 58. 2017 data mining projects with source code 59. 2017 data mining projects for final year students 60. 2017 data mining projects in java 61. 2017 data mining projects for students
Views: 14834 InnovationAdsOfIndia
Artificial Intelligence - Genetic Algorithm
 
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In this video you can get a sense of how an algorithm example works, for more detail search the AllGen application on Google Play.
Views: 488 Edmar Rocha
Genetic Algorithm problem with solution | G.A Maximize f(x)= x^2 .Show one Crossover? Soft Computing
 
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Welcome Guys, we will see How to find Genetic Algorithm Maximize f(x)= x^2. Show one Crossover? In soft computing in Hindi. Genetic Algorithm problem with a solution. If you like my video plz LIKE, SHARE & SUBSCRIBE my THAPATECHNICAL channel :)
Views: 22814 Thapa Technical
What is EVOLUTIONARY DATA MINING? What does EVOLUTIONARY DATA MINING mean?
 
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What is EVOLUTIONARY DATA MINING? What does EVOLUTIONARY DATA MINING mean? EVOLUTIONARY DATA MINING meaning - EVOLUTIONARY DATA MINING definition - EVOLUTIONARY DATA MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Evolutionary data mining, or genetic data mining is an umbrella term for any data mining using evolutionary algorithms. While it can be used for mining data from DNA sequences, it is not limited to biological contexts and can be used in any classification-based prediction scenario, which helps "predict the value ... of a user-specified goal attribute based on the values of other attributes." For instance, a banking institution might want to predict whether a customer's credit would be "good" or "bad" based on their age, income and current savings. Evolutionary algorithms for data mining work by creating a series of random rules to be checked against a training dataset. The rules which most closely fit the data are selected and are mutated. The process is iterated many times and eventually, a rule will arise that approaches 100% similarity with the training data. This rule is then checked against a test dataset, which was previously invisible to the genetic algorithm. Before databases can be mined for data using evolutionary algorithms, it first has to be cleaned, which means incomplete, noisy or inconsistent data should be repaired. It is imperative that this be done before the mining takes place, as it will help the algorithms produce more accurate results. If data comes from more than one database, they can be integrated, or combined, at this point. When dealing with large datasets, it might be beneficial to also reduce the amount of data being handled. One common method of data reduction works by getting a normalized sample of data from the database, resulting in much faster, yet statistically equivalent results. At this point, the data is split into two equal but mutually exclusive elements, a test and a training dataset. The training dataset will be used to let rules evolve which match it closely. The test dataset will then either confirm or deny these rules. Evolutionary algorithms work by trying to emulate natural evolution. First, a random series of "rules" are set on the training dataset, which try to generalize the data into formulas. The rules are checked, and the ones that fit the data best are kept, the rules that do not fit the data are discarded. The rules that were kept are then mutated, and multiplied to create new rules. This process iterates as necessary in order to produce a rule that matches the dataset as closely as possible. When this rule is obtained, it is then checked against the test dataset. If the rule still matches the data, then the rule is valid and is kept. If it does not match the data, then it is discarded and the process begins by selecting random rules again.
Views: 214 The Audiopedia
Genetic Programming in Java with TinyGP (Part 6 - Data Mining)
 
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A continuing series on Riccardo Poli's TinyGP Java program. In this installment, we make a minor modification by refactoring TinyGP with three logic operators in order to allow the program to do some basic data mining of relationships between input values to a target value. A very obvious toy scenario is first introduced, and then a more involved scenario is built, a formula derived, and an analysis done by with a simple spreadsheet.
Views: 466 Brint Montgomery
Crossover Example - Georgia Tech - Machine Learning
 
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Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud262/l-521298714/m-534408629 Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud262 Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Views: 10852 Udacity
Rapidminer 5.0 Video Tutorial #4 - Genetic Optimization Part 1
 
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In this video I highlight the data generation capabilities for Rapidminer 5.0 if you want to tinker around, and how to use a Genetic Optimization data pre-processor within a nested nested experiment. Yes, you read that correctly, a nested nested experiment.
Views: 22136 NeuralMarketTrends
Genetic Algorithms Tutorial 04 - Class Scheduling JAVA Application
 
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Website + download source code @ http://www.zaneacademy.com
Views: 22653 zaneacademy
Weka Tutorial 10: Feature Selection with Filter (Data Dimensionality)
 
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This tutorial shows how to select features from a set of features that performs best with a classification algorithm using filter method.
Views: 68476 Rushdi Shams
GA in R
 
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Hello, My name is Elham Taghizadeh This video is my first video related to GA in R.
Views: 2346 Elham Taghizade
Music Theory - Genetic Algorithms and Python
 
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[EuroPython 2012] Nicolas Tollervey - 4 JULY 2012 in "Track Lasagne"
Views: 46083 EuroPython Conference
Feature Selection - Forward, Backward, Stepwise & Genetic Algorithm
 
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Links GitHub: https://github.com/jk6653284/python_feature_and_model MatLab: https://www.youtube.com/watch?v=1i8muvzZkPw
Views: 1068 Louis Rampignon
Optimized Association Rule Mining with Genetic Algorithms
 
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The mechanism for unearthing hidden facts in large datasets and drawing inferences on how a subset of items influences the presence of another subset is known as Association Rule Mining (ARM). There is a wide variety of rule interestingness metrics that can be applied in ARM. Due to the wide range of rule quality metrics it is hard to determine which are the most `interesting' or `optimal' rules in the dataset. In this paper we propose a multi-objective approach to generating optimal association rules using two new rule quality metrics: syntactic superiority and transactional superiority. These two metrics ensure that dominated but interesting rules are returned to not eliminated from the resulting set of rules.
ELSVIER 2013 NS2 Dynamic genetic algorithms for the dynamic load balanced clustering problem in mobi
 
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PG Embedded Systems #197 B, Surandai Road Pavoorchatram,Tenkasi Tirunelveli Tamil Nadu India 627 808 Tel:04633-251200 Mob:+91-98658-62045 General Information and Enquiries: [email protected] [email protected] PROJECTS FROM PG EMBEDDED SYSTEMS 2013 ieee projects, 2013 ieee java projects, 2013 ieee dotnet projects, 2013 ieee android projects, 2013 ieee matlab projects, 2013 ieee embedded projects, 2013 ieee robotics projects, 2013 IEEE EEE PROJECTS, 2013 IEEE POWER ELECTRONICS PROJECTS, ieee 2013 android projects, ieee 2013 java projects, ieee 2013 dotnet projects, 2013 ieee mtech projects, 2013 ieee btech projects, 2013 ieee be projects, ieee 2013 projects for cse, 2013 ieee cse projects, 2013 ieee it projects, 2013 ieee ece projects, 2013 ieee mca projects, 2013 ieee mphil projects, tirunelveli ieee projects, best project centre in tirunelveli, bulk ieee projects, pg embedded systems ieee projects, pg embedded systems ieee projects, latest ieee projects, ieee projects for mtech, ieee projects for btech, ieee projects for mphil, ieee projects for be, ieee projects, student projects, students ieee projects, ieee proejcts india, ms projects, bits pilani ms projects, uk ms projects, ms ieee projects, ieee android real time projects, 2013 mtech projects, 2013 mphil projects, 2013 ieee projects with source code, tirunelveli mtech projects, pg embedded systems ieee projects, ieee projects, 2013 ieee project source code, journal paper publication guidance, conference paper publication guidance, ieee project, free ieee project, ieee projects for students., 2013 ieee omnet++ projects, ieee 2013 oment++ project, innovative ieee projects, latest ieee projects, 2013 latest ieee projects, ieee cloud computing projects, 2013 ieee cloud computing projects, 2013 ieee networking projects, ieee networking projects, 2013 ieee data mining projects, ieee data mining projects, 2013 ieee network security projects, ieee network security projects, 2013 ieee image processing projects, ieee image processing projects, ieee parallel and distributed system projects, ieee information security projects, 2013 wireless networking projects ieee, 2013 ieee web service projects, 2013 ieee soa projects, ieee 2013 vlsi projects, NS2 PROJECTS,NS3 PROJECTS. DOWNLOAD IEEE PROJECTS: 2013 IEEE java projects,2013 ieee Project Titles, 2013 IEEE cse Project Titles, 2013 IEEE NS2 Project Titles, 2013 IEEE dotnet Project Titles. IEEE Software Project Titles, IEEE Embedded System Project Titles, IEEE JavaProject Titles, IEEE DotNET ... IEEE Projects 2013 - 2013 ... Image Processing. IEEE 2013 - 2013 Projects | IEEE Latest Projects 2013 - 2013 | IEEE ECE Projects2013 - 2013, matlab projects, vlsi projects, software projects, embedded. eee projects download, base paper for ieee projects, ieee projects list, ieee projectstitles, ieee projects for cse, ieee projects on networking,ieee projects. Image Processing ieee projects with source code, Image Processing ieee projectsfree download, Image Processing application projects free download. .NET Project Titles, 2013 IEEE C#, C Sharp Project Titles, 2013 IEEE EmbeddedProject Titles, 2013 IEEE NS2 Project Titles, 2013 IEEE Android Project Titles. 2013 IEEE PROJECTS, IEEE PROJECTS FOR CSE 2013, IEEE 2013 PROJECT TITLES, M.TECH. PROJECTS 2013, IEEE 2013 ME PROJECTS.
Views: 739 PG Embedded Systems
Interview with Prof. Dr. Thomas Bäck -- about using Data Mining to create Innovations
 
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Short Interview with Prof. Dr. Thomas Bäck about using Data Mining for Innovations. e.g. genetic / evolutionary algorithms to solve multi-objective problems. Thomas is CEO of DIVIS www.divis-gmbh.de and I met him at the Marcus Evans Conference in Cologne November 2012.
Views: 390 Fabian Schlage
Evolution 3.0 : solve your everyday problems with genetic algorithm / Mey Maayan Akiva
 
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When was the last time you wrote an algorithm to plan your diet? As programmers we do amazing things in our everyday job, but rarely do we use our knowledge at home. In this talk I will introduce genetic algorithms and share how I coded a genetic algorithm from scratch and used it to generate my weekly schedule and to create a smart diet planer. We will go through the different stages of the algorithm and understand how they affect the algorithm’s solutions. Let me show you a different side of genetic algorithms and you will discover a new way to solve your everyday problems. https://summit2018.reversim.com/session/5b0d4dfc4b330d00147e3374
Views: 904 Reversim

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