Search results “Genetic algorithms and data mining”

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Well Academy

A brief introduction to genetic algorithms with examples.

Views: 125189
chriskam1250

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: 10764
Udacity

(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: 16555
David Stepanov

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: 142500
nptelhrd

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: 106699
MATLAB

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: 10729
Ahsan Ashfaq

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: 174248
The Coding Train

Gentle introduction to genetic algorithms based on AI A modern approach (by Russel and Norvig).

Views: 11655
Francisco Iacobelli

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: 312424
MIT OpenCourseWare

Supervised and unsupervised learning algorithms

Views: 55428
Nathan Kutz

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: 21057
Derek Kane

Title: Automatic Time Table Generation Using Genetic Algorithm
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Views: 12028
InnovationAdsOfIndia

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: 424
Brint Montgomery

Website + download source code @ http://www.zaneacademy.com

Views: 1788
zaneacademy

Website + download source code @ http://www.zaneacademy.com

Views: 889
zaneacademy

Hello, My name is Elham Taghizadeh
This video is my first video related to GA in R.

Views: 1274
Elham Taghizade

Order now: https://goo.gl/TIo1T2?85794

Views: 49
Валентин Казанцев

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
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E2MATRIX RESEARCH LAB

( Data Science Training - https://www.edureka.co/data-science )
This tutorial will give you an overview of the most common algorithms that are used in Data Science. Here, you will learn what activities Data Scientists do and you will learn how they use algorithms like Decision Tree, Random Forest, Association Rule Mining, Linear Regression and K-Means Clustering. To learn more about Data Science click here: http://goo.gl/9HsPlv
The topics related to 'R', Machine learning and Hadoop and various other algorithms have been extensively covered in our course “Data Science”.
For more information, please write back to us at [email protected]
Call us at US: 1800 275 9730 (toll free) or India: +91-8880862004

Views: 93719
edureka!

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: 55086
nptelhrd

Website + download source code @ http://www.zaneacademy.com

Views: 16689
zaneacademy

Website + download source code @ http://www.zaneacademy.com | Genetic Algorithms w/ Python - Tutorial 01 @ https://youtu.be/zumC_C0C25c

Views: 22626
zaneacademy

This lecture discusses basic principles of Genetic Algorithms, a class of evolutionary computational algorithms. Genetic algorithms are based on the concept of biological evolution. As in biological science there is concept of how species are born due to union of male and female type of species and new generation take place of older generation, in the similar way due to union of candidate solutions [ chromosomes ] new candidate solutions [ chromosomes ] are being produced. New candidate solutions may mutate and may be ignored in new population if they do not meet the good-fit criteria. It is similar to Darwin's "survival-of-fittest" theory. New chromosomes [ candidate solutions ] which are good fit or desirable [ better children ] will move forward to next level of evolution and rest will be discarded. This evolution loop continues till the desired quality solution is archived. These algorithms are mainly used for NP-Hard kind of problems and provide approximate solutions to the problem.
[ ►Subscribe ] Leprofesseur } on YouTube. We appreciate your feedback and support. Do not forget to give thumbs-up 🙂
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Harrish

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LEPROFESSEUR

Contact - 08975313145

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Codeengine

For More Explanation And Techniques
Contact:K.Manjunath,9535866270,
http://www.tmksinfotech.com
Bangalore,Karnataka.

Views: 928
manju nath

[EuroPython 2012] Nicolas Tollervey - 4 JULY 2012 in "Track Lasagne"

Views: 44955
EuroPython Conference

This was a video response to the genetic algorithm videos you posted. I would like to tell you about my project of combining computer science with genomics.
Is it possible for a computer or Artificial Intelligence to predict genotype based on phenotype?
It was dinner time at home, so please excuse all the kitchen noises.

Views: 125
titanoboa100

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: 368
Fabian Schlage

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.

Views: 395
Final Year Solutions

Views: 24225
Machine Learning- Sudeshna Sarkar

PyData Chicago 2016
Slides: http://www.slideshare.net/secret/dvt9zZBUVz7b7X
Github: https://github.com/esander91
Code: https://github.com/esander91/GoodEnoughAlgs.
Evolutionary algorithms let us tackle all kinds of impossible problems. Want to design a short delivery route, but there are more possible solutions than atoms in the universe? Well, evolutionary algorithms can't promise to find the optimal solution, but can guarantee finding a pretty great one. I'll give an overview of these algorithms, and how you can use them for your own impossible problems.

Views: 1516
PyData

A very simple Explanation of Particle Swarm Optimization . This tutorial is a very simple explanation of PSO, a population based optimization technique by Red Apple Tutorials.
Watch Part 2 @ https://youtu.be/0-h3tuAMv-8

Views: 21417
Red Apple Tutorials

Contact : 9606359471

Views: 52
vihaan sudhan

In this video I describe how the k Nearest Neighbors algorithm works, and provide a simple example using 2-dimensional data and k = 3.

Views: 338101
Thales Sehn Körting

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Views: 33
GradeSetter Private Limited

Optical Kerr signal fitting of CS2 using genetic algorithms

Views: 83
Eduardo Ochoa

A continuing series on Riccardo Poli's TinyGP Java program. In this installment, we analyze how fitness interacts with tournament selection.

Views: 1610
Brint Montgomery

This tutorial has steps in genetic algorithm

Views: 9849
Red Apple Tutorials

Algorithmic Currency Forecast: The table on the left is the forex forecast for the forex outlook, produced by I Know First's algorithm. Each day, subscribers receive forecasts for six different time horizons. The currencies in the 1-month forecast may be different than those in the 1-year forecast. In the included table, only the relevant currencies have been included. A green box represents a positive forecast while a red represents a negative forecast. The boxes are then arranged according to their respective signal and predictability values (see below for detailed definitions).
Read Full Article Here: https://iknowfirst.com/fr-currency-forecast-based-on-genetic-algorithms-72-22-hit-ratio-in-3-days
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Views: 52
I Know First: Daily Market Forecast

© 2018 Org apache maven plugin resources resource

If fuel, battery backup power or batteries are required, make sure the system can run for the required time and chargers are available. Document how to operate these systems and mark the locations of controls. Make sure the information is available during an emergency. Many of these systems also require periodic inspection, testing and maintenance in accordance with national codes and standards. Train staff so a knowledgeable person is able to operate systems and equipment. Materials and Supplies. Be sure to compile a list of available resources using the Emergency Response Resource Requirements and Business Continuity Resource Requirements worksheets as a guide. External Resources. Preparing for an emergency, responding to an emergency, executing business recovery strategies and other activities require resources that come from outside the business. If there were a fire in the building, you would call the fire department. Contractors and vendors may be needed to prepare a facility for a forecast storm or to help repair and restore a building, systems or equipment following an incident. The following external resources should be identified within plan documents. Include contact information to reach them during an emergency and any additional instructions within the preparedness plan. Public Emergency Services. Contractors and Vendors. Partnerships.