Guest Lecture about genetic algorithms in the course ECE657: Computational Intelligence/Intelligent Systems Design, Spring 2016, Electrical and Computer Engineering (ECE) Department, University of Waterloo, Canada.
For three decades, many mathematical programming methods have been developed to solve optimization problems. However, until now, there has not been a single totally efficient and robust method to coverall optimization problems that arise in the different engineering fields.Most engineering application design problems involve the choice of design variable values that better describe the behaviour of a system.At the same time, those results should cover the requirements and specifications imposed by the norms for that system. This last condition leads to predicting what the entrance parameter values should be whose design results comply with the norms and also present good performance, which describes the inverse problem.Generally, in design problems the variables are discreet from the mathematical point of view. However, most mathematical optimization applications are focused and developed for continuous variables. Presently, there are many research articles about optimization methods; the typical ones are based on calculus,numerical methods, and random methods.
The calculus-based methods have been intensely studied and are subdivided in two main classes: 1) the direct search methods find a local maximum moving a function over the relative local gradient directions and 2) the indirect methods usually find the local ends solving a set of non-linear equations, resultant of equating the gradient from the object function to zero, i.e., by means of multidimensional generalization of the notion of the function’s extreme points from elementary calculus given smooth function without restrictions to find a possible maximum which is to be restricted to those points whose slope is zero in all directions. The real world has many discontinuities and noisy spaces, which is why it is not surprising that the methods depending upon the restrictive requirements of continuity and existence of a derivative, are unsuitable for all, but a very limited problem domain. A number of schemes have been applied in many forms and sizes. The idea is quite direct inside a finite search space or a discrete infinite search space, where the algorithms can locate the object function values in each space point one at a time. The simplicity of this kind of algorithm is very attractive when the numbers of possibilities are very small. Nevertheless, these outlines are often inefficient, since they do not complete the requirements of robustness in big or highly-dimensional spaces, making it quite a hard task to find the optimal values. Given the shortcomings of the calculus-based techniques and the numerical ones the random methods have increased their popularity.
Genetic Algorithm (GA) is a search-based optimization technique based on the principles of Genetics and Natural Selection. It is frequently used to find optimal or near-optimal solutions to difficult problems which otherwise would take a lifetime to solve. It is frequently used to solve optimization problems, in research, and in machine learning.
Guest Lecture about genetic algorithms in the course ECE657: Computational Intelligence/Intelligent Systems Design, Spring 2016, Electrical and Computer Engineering (ECE) Department, University of Waterloo, Canada.
For three decades, many mathematical programming methods have been developed to solve optimization problems. However, until now, there has not been a single totally efficient and robust method to coverall optimization problems that arise in the different engineering fields.Most engineering application design problems involve the choice of design variable values that better describe the behaviour of a system.At the same time, those results should cover the requirements and specifications imposed by the norms for that system. This last condition leads to predicting what the entrance parameter values should be whose design results comply with the norms and also present good performance, which describes the inverse problem.Generally, in design problems the variables are discreet from the mathematical point of view. However, most mathematical optimization applications are focused and developed for continuous variables. Presently, there are many research articles about optimization methods; the typical ones are based on calculus,numerical methods, and random methods.
The calculus-based methods have been intensely studied and are subdivided in two main classes: 1) the direct search methods find a local maximum moving a function over the relative local gradient directions and 2) the indirect methods usually find the local ends solving a set of non-linear equations, resultant of equating the gradient from the object function to zero, i.e., by means of multidimensional generalization of the notion of the function’s extreme points from elementary calculus given smooth function without restrictions to find a possible maximum which is to be restricted to those points whose slope is zero in all directions. The real world has many discontinuities and noisy spaces, which is why it is not surprising that the methods depending upon the restrictive requirements of continuity and existence of a derivative, are unsuitable for all, but a very limited problem domain. A number of schemes have been applied in many forms and sizes. The idea is quite direct inside a finite search space or a discrete infinite search space, where the algorithms can locate the object function values in each space point one at a time. The simplicity of this kind of algorithm is very attractive when the numbers of possibilities are very small. Nevertheless, these outlines are often inefficient, since they do not complete the requirements of robustness in big or highly-dimensional spaces, making it quite a hard task to find the optimal values. Given the shortcomings of the calculus-based techniques and the numerical ones the random methods have increased their popularity.
Genetic Algorithm (GA) is a search-based optimization technique based on the principles of Genetics and Natural Selection. It is frequently used to find optimal or near-optimal solutions to difficult problems which otherwise would take a lifetime to solve. It is frequently used to solve optimization problems, in research, and in machine learning.
Analysis of Parameter using Fuzzy Genetic Algorithm in E-learning SystemHarshal Jain
The aim of this project is to analyze the parameter, for the inputs to find an optimization problem than the candidate solution we have. This will help us to find more accurate knowledge level of user, using Genetic Algorithm (GA). In this algorithm a population of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem is evolved toward better solutions.
This presentation is intended for giving an introduction to Genetic Algorithm. Using an example, it explains the different concepts used in Genetic Algorithm. If you are new to GA or want to refresh concepts , then it is a good resource for you.
Contents of the presentation:
• GA – Introduction
• GA – Fundamentals
• GA – Genotype Representation
• GA – Population
• GA – Fitness Function
• GA – Parent Selection
• GA – Crossover
• GA – Mutation
• Research Paper
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection.
Presentation is about genetic algorithms. Also it includes introduction to soft computing and hard computing. Hope it serves the purpose and be useful for reference.
Analysis of Parameter using Fuzzy Genetic Algorithm in E-learning SystemHarshal Jain
The aim of this project is to analyze the parameter, for the inputs to find an optimization problem than the candidate solution we have. This will help us to find more accurate knowledge level of user, using Genetic Algorithm (GA). In this algorithm a population of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem is evolved toward better solutions.
This presentation is intended for giving an introduction to Genetic Algorithm. Using an example, it explains the different concepts used in Genetic Algorithm. If you are new to GA or want to refresh concepts , then it is a good resource for you.
Contents of the presentation:
• GA – Introduction
• GA – Fundamentals
• GA – Genotype Representation
• GA – Population
• GA – Fitness Function
• GA – Parent Selection
• GA – Crossover
• GA – Mutation
• Research Paper
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection.
Presentation is about genetic algorithms. Also it includes introduction to soft computing and hard computing. Hope it serves the purpose and be useful for reference.
Combining Competent Crossover and Mutation Operators: A Probabilistic Model B...kknsastry
This paper presents an approach to combine competent crossover and mutation operators via probabilistic model building. Both operators are based on the probabilistic model building procedure of the extended compact genetic algorithm (eCGA). The model sampling procedure of eCGA, which mimics the behavior of an idealized recombination—where the building blocks (BBs) are exchanged without disruption—is used as the competent crossover operator. On the other hand, a recently proposed BB-wise mutation operator—which uses the BB partition information to perform local search in the BB space—is used as the competent mutation operator. The resulting algorithm, called hybrid extended compact genetic algorithm (heCGA), makes use of the problem decomposition information for (1) effective recombination of BBs and (2) effective local search in the BB neighborhood. The proposed approach is tested on different problems that combine the core of three well known problem difficulty dimensions: deception, scaling, and noise. The results show that, in the absence of domain knowledge, the hybrid approach is more robust than either single-operator-based approach.
Genetic Algorithms and Genetic Programming for Multiscale Modelingkknsastry
Effective and efficient multiscale modeling is essential to advance both the science and synthesis in a wide array of fields such as physics, chemistry, materials science, biology, biotechnology and pharmacology. This study investigates the efficacy and potential of using genetic algorithms for multiscale materials modeling and
addresses some of the challenges involved in designing competent algorithms that solve hard problems quickly, reliably
and accurately.
An intelligent scalable stock market prediction systemHarshit Agarwal
Comparitive study of stock market prediction system using ANN and GONN. Sentiment analysis also done on yahoo news feed. Deployment done on hadoop cluster.
Talk on the genetic and genomic evaluation system for US dairy cattle made to scientists at Embrapa Gado de Leite in Juiz de Fora, MG, Brasil, on September 10, 2014.
Genetic evaluation and best prediction of lactation persistencyJohn B. Cole, Ph.D.
At the same level of production cows with high persistency milk more at the end than the beginning of lactation. Best prediction of persistency is calculated as a function of trait-specific standard lactation curves and the linear regression of a cow’s test day deviations on days in milk.
STAG Software presented a webinar on Aug 21, 2013 on the topic - Improving Defect Yield - a three step approach". The webinar was hosted by T Ashok, Founder & CEO, STAG Software and Architect of HBT.
Rise of the Machines: Can Artificial Intelligence Terminate Manual Testing?TechWell
The state of the art in automated software testing is far from being a replacement for human-guided testing. There is more to testing than setting up preconditions, applying inputs, verifying outputs, and logging the results. Testing requires significant planning, exploring, learning, modeling, inferencing, experimenting, and more. Therefore, before we can truly automate testing, we must bridge the gap between the testing capabilities of humans and machines. Tariq King says that breakthroughs in artificial intelligence (AI) and machine learning (ML) are challenging our thinking about the types of problems that machines can tackle. Can AI discoveries—a machine that masters a game like Go or autonomously drives an unmanned vehicle—help us find better solutions for automated oracles, test generation, system modeling, and defect discovery? Tariq believes they can and will share his vision of how. Drawing on his experiences working on, leading, and advising teams in the development of software that automatically tests software, Tariq walks us through recent advances in AI and ML. Join Tariq as he maps these advances to potential solutions for important software testing research problems.
Software robots like monkey provide a quick way to validate your application. With robots on new cloud testing services, it is easier than ever to get started testing your app without even having written any tests. In this talk, I will introduce a few tools from both academia and industry, and then cover the basics of how these tools work. You will learn about the strengths and limitations these tools and how to use them effectively to maximize code coverage and catching failures.
MRG Effitas is an independent IT security research company, with a heavy focus on applied malware analysis. Besides conventional AV efficacy testing and providing samples to other players in the AV field, we regularly test APT appliances and enterprise grade IT security products simulating realistic attack scenarios. In this regard, testing methods have evolved rapidly over the last couple of years as most labs, under the guidance of AMTSO (of which MRG Effitas is a
member) strived to conduct “Real World” testing.
Trapmine approached us with a request to put their anti-exploit defense framework (TRAPMINE) to test. The following report summarizes the description of our testing efforts, our samples and the results.
Trapmine Inc. is an innovative endpoint security solution provider mainly focusing on protecting organizations from Advanced Persistent Threat & Zero-Day attacks. Founders of Trapmine are well-known vulnerability researchers in the industry who know how to break systems ethically and decided to use their offensive cyber security skills to protect enterprises.
Test First Refresh Second: Test-Driven Development in GrailsTim Berglund
Grails provides solid support for unit testing of parts of your application that are usually very difficult to test. Learn how to enable test-first development practices using the Grails framework.
Starting with the premise that "Performance is a Feature", Matt Warren will show you how to measure, what to measure and how to get the best performance from your .NET code.
We will look at real-world examples from the Roslyn code-base and StackOverflow (the product), including how the .NET Garbage Collector needs to be tamed!
The presentation covers:
Why we should care about performance
Pitfalls to avoid when measuring performance
How the .NET Garbage Collector can hurt performance
Real-world performance lessons from open-source code
The webinar recording can be found here: http://www.postsharp.net/blog/post/webinar-recording-performance-is-a-feature
The field of machine programming — the automation of the development of software — is making notable research advances. This is, in part, due to the emergence of a wide range of novel techniques in machine learning. In today’s technological landscape, software is integrated into almost everything we do, but maintaining software is a time-consuming and error-prone process. When fully realized, machine programming will enable everyone to express their creativity and develop their own software without writing a single line of code. Intel realizes the pioneering promise of machine programming, which is why it created the Machine Programming Research (MPR) team in Intel Labs. The MPR team’s goal is to create a society where everyone can create software, but machines will handle the “programming” part.
Graduation Project - Face Login : A Robust Face Identification System for Sec...Ahmed Gad
Face login is my 2015 graduation project started in 2014 and lasted 1.5 years of work.
Generally, it is an identification system using face images. It is a multi-use system but it was mainly created to authorize users to login into their system.
There is an IEEE paper published by the project algorithm used in ICCES 2014 http://ieeexplore.ieee.org/abstract/document/7030929/.
Here is its citation Semary, Noura A., and Ahmed Fawzi Gad. "A proposed framework for robust face identification system." Computer Engineering & Systems (ICCES), 2014 9th International Conference on. IEEE, 2014.
A YouTube video describing the project generally.
https://www.youtube.com/watch?v=OUvaPW70Eko
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Similar to Introduction to Genetic Programming (20)
This is inspired from Tom Mitchell's book on Machine Learning. You can achieve a bit exact implementation of the back propagation algorithm if you follow the code in this.
A simple client-server application in java in which a client sends a message to a server and the server tries to be funny by sending back a funny response.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
7. Introduction
Experimental Setup
Genetic Operators
Applications
GP Life Cycle: The Pseudo Code
1 Create an initial population of computer programs.
2 Evaluation.
3 Selection.
4 Reproduction.
5 Evaluation.
6 Replacement.
7 Continue from 3.
Muhammad Adil Raja Genetic Programming
9. Introduction
Experimental Setup
Genetic Operators
Applications
TABLE: Fiddle Parameters of a Typical GP Experiment
Parameter Value
Initial Population Size 300
Initial Tree Depth 6
Selection LPP
Tournament Size 2
Genetic Operators Crossover and Subtree Mutation
Operators Probability Type Adaptive
Initial Operator probabilities 0.5 each
Survival Half Elitism
Generation Gap 1
Function Set plus, minus, multiply, divide,sin,
cos, log2, log10, loge, sqrt,
power, if
Terminal Set Random real-valued numbers
between 0.0 and 1.0. Integers (2–10) and
Network traffic parameters from Table.
Muhammad Adil Raja Genetic Programming
16. Introduction
Experimental Setup
Genetic Operators
Applications
APPLICATIONS
In regression and classification.
Regression or Classification of nonlinear problems.
In Telecommunications: Speech quality estimation.
In Computer Networks: Network coding.
In Finance: In evolving effective bidding strategies.
In Clinical: Cancer detectors, seizure detectors, mental
health diagnosis etc.
Muhammad Adil Raja Genetic Programming