This presentation discusses the following topics:What is Genetic Algorithms?
Introduction to Genetic Algorithm
Classes of Search Techniques
Components of a GA
Components of a GA
Simple Genetic Algorithm
GA Cycle of Reproduction
Population
Reproduction
Chromosome Modification: Mutation, Crossover, Evaluation, Deletion
Example
GA Technology
Issues for GA Practitioners
Benefits of Genetic Algorithms
GA Application Types
This document provides an introduction to genetic algorithms. It explains that genetic algorithms are inspired by Darwinian evolution and use processes like selection, crossover and mutation to iteratively improve a population of potential solutions. It discusses how genetic algorithms can be used for optimization problems and classification in data mining. Examples of genetic algorithm applications like the traveling salesman problem are also presented to illustrate genetic algorithm concepts and processes.
The document introduces genetic algorithms, which are inspired by biological evolution. It describes how genetic algorithms use operations like selection, crossover and mutation to evolve solutions to problems in a way that is analogous to natural selection. It also outlines the basic components of a genetic algorithm, including representing solutions, initializing a population, evaluating fitness, and selecting solutions to breed new generations. Finally, it discusses some common applications of genetic algorithms to optimization problems.
Genetic algorithms (GA) are a class of optimization algorithms inspired by biological evolution. GAs use concepts like natural selection and genetic inheritance to evolve solutions to problems by iteratively selecting better solutions. A GA encodes potential solutions as strings called chromosomes and uses genetic operators like crossover and mutation to generate new solutions, evaluating them to select the fittest ones. This process is repeated until a termination condition is reached, such as a solution meeting criteria or a fixed number of generations. GAs are well-suited for complex problems where little is known about the search space.
This document provides an overview of genetic algorithms. It discusses that genetic algorithms are a type of evolutionary algorithm inspired by biological evolution that is used to find optimal or near-optimal solutions to problems by mimicking natural selection. The document outlines the basic concepts of genetic algorithms including encoding, representation, search space, fitness functions, and the main operators of selection, crossover and mutation. It also provides examples of applications in bioinformatics and highlights advantages like being easy to understand while also noting potential disadvantages like requiring more computational time.
The GENETIC ALGORITHM is a model of machine learning which derives its behavior from a metaphor of the processes of EVOLUTION in nature. Genetic Algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems.
This presentation discusses the following Fuzzy logic concepts:
Introduction
Crisp Variables
Fuzzy Variables
Fuzzy Logic Operators
Fuzzy Control
Case Study
Genetic algorithms are optimization techniques inspired by Darwin's theory of evolution. They use operations like selection, crossover and mutation to evolve solutions to problems by iteratively trying random variations. The document outlines the history, concepts, process and applications of genetic algorithms, including using them to optimize engineering design, routing, computer games and more. It describes how genetic algorithms encode potential solutions and use fitness functions to guide the evolution toward better outcomes.
This document provides an introduction to genetic algorithms. It explains that genetic algorithms are inspired by Darwinian evolution and use processes like selection, crossover and mutation to iteratively improve a population of potential solutions. It discusses how genetic algorithms can be used for optimization problems and classification in data mining. Examples of genetic algorithm applications like the traveling salesman problem are also presented to illustrate genetic algorithm concepts and processes.
The document introduces genetic algorithms, which are inspired by biological evolution. It describes how genetic algorithms use operations like selection, crossover and mutation to evolve solutions to problems in a way that is analogous to natural selection. It also outlines the basic components of a genetic algorithm, including representing solutions, initializing a population, evaluating fitness, and selecting solutions to breed new generations. Finally, it discusses some common applications of genetic algorithms to optimization problems.
Genetic algorithms (GA) are a class of optimization algorithms inspired by biological evolution. GAs use concepts like natural selection and genetic inheritance to evolve solutions to problems by iteratively selecting better solutions. A GA encodes potential solutions as strings called chromosomes and uses genetic operators like crossover and mutation to generate new solutions, evaluating them to select the fittest ones. This process is repeated until a termination condition is reached, such as a solution meeting criteria or a fixed number of generations. GAs are well-suited for complex problems where little is known about the search space.
This document provides an overview of genetic algorithms. It discusses that genetic algorithms are a type of evolutionary algorithm inspired by biological evolution that is used to find optimal or near-optimal solutions to problems by mimicking natural selection. The document outlines the basic concepts of genetic algorithms including encoding, representation, search space, fitness functions, and the main operators of selection, crossover and mutation. It also provides examples of applications in bioinformatics and highlights advantages like being easy to understand while also noting potential disadvantages like requiring more computational time.
The GENETIC ALGORITHM is a model of machine learning which derives its behavior from a metaphor of the processes of EVOLUTION in nature. Genetic Algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems.
This presentation discusses the following Fuzzy logic concepts:
Introduction
Crisp Variables
Fuzzy Variables
Fuzzy Logic Operators
Fuzzy Control
Case Study
Genetic algorithms are optimization techniques inspired by Darwin's theory of evolution. They use operations like selection, crossover and mutation to evolve solutions to problems by iteratively trying random variations. The document outlines the history, concepts, process and applications of genetic algorithms, including using them to optimize engineering design, routing, computer games and more. It describes how genetic algorithms encode potential solutions and use fitness functions to guide the evolution toward better outcomes.
Evolutionary algorithms are stochastic search and optimization heuristics derived from the classic evolution theory, which are implemented on computers in the majority of cases.
This document provides an overview of fuzzy logic, including its origins, key concepts, and applications. It discusses how fuzzy logic allows for approximate reasoning and decision making under uncertainty by using linguistic variables and fuzzy set theory. Membership functions are used to characterize fuzzy sets and assign degrees of truth between 0 and 1 rather than binary true/false values. Common fuzzy logic operations like intersection, union, and complement are also covered. Finally, some examples of fuzzy logic control systems are presented, including how they are designed using fuzzy rule bases and inference methods like Mamdani and Sugeno.
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.
Nature-Inspired Optimization Algorithms Xin-She Yang
This document discusses nature-inspired optimization algorithms. It begins with an overview of the essence of optimization algorithms and their goal of moving to better solutions. It then discusses some issues with traditional algorithms and how nature-inspired algorithms aim to address these. Several nature-inspired algorithms are described in detail, including particle swarm optimization, firefly algorithm, cuckoo search, and bat algorithm. These are inspired by behaviors in swarms, fireflies, cuckoos, and bats respectively. Examples of applications to engineering design problems are also provided.
This presentation discusses the following ANN concepts:
Introduction
Characteristics
Learning methods
Taxonomy
Evolution of neural networks
Basic models
Important technologies
Applications
Evolutionary Computing is a research area within computer science. As the name suggest, it is a special flavour of computing, which draws inspiration from the process of natural evolution. The fundamental metaphor of evolutionary computing relates this powerful natural evolution to a particular style of problem solving – that of trial and error.
Genetic algorithms are a type of evolutionary algorithm inspired by Darwin's theory of evolution. They use operations like selection, crossover and mutation to evolve solutions to problems over multiple generations. Genetic algorithms work on a population of potential solutions encoded as chromosomes, evolving them toward better solutions. They have been applied to optimization and search problems in various domains like robotics, engineering and bioinformatics.
Soft computing is an approach to computing that aims to model human-like decision making. It deals with imprecise or uncertain data using techniques like fuzzy logic, neural networks, and genetic algorithms. The goal is to develop systems that are tolerant of imprecision, uncertainty, and approximation to achieve practical and low-cost solutions to real-world problems. Soft computing was initiated in 1981 and includes fields like fuzzy logic, neural networks, and evolutionary computation. It provides approximate solutions using techniques like neural network reasoning, genetic programming, and functional approximation.
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.
This document discusses genetic algorithms and their applications. It explains key concepts like genetic crossover, genetic algorithm steps to solve optimization problems, and how genetic algorithms mimic biological evolution. Examples are provided of genetic algorithms being used for tasks like predicting protein structure, automotive design optimization, and generating musical variations. Advantages and limitations of genetic algorithms are also summarized.
The document discusses artificial neural networks and backpropagation. It provides an overview of backpropagation algorithms, including how they were developed over time, the basic methodology of propagating errors backwards, and typical network architectures. It also gives examples of applying backpropagation to problems like robotics, space robots, handwritten digit recognition, and face recognition.
Following topics are discussed in this presentation:What is Soft Computing?
What is Hard Computing?
What is Fuzzy Logic Models?
What is Neural Networks (NN)?
What is Genetic Algorithms or Evaluation Programming?
What is probabilistic reasoning?
Difference between fuzziness and probability
AI and Soft Computing
Future of Soft Computing
Genetic algorithms are inspired by Darwin's theory of natural selection and use techniques like inheritance, mutation, and selection to find optimal solutions. The document discusses genetic algorithms and their application in data mining. It provides examples of how genetic algorithms use selection, crossover, and mutation operators to evolve rules for predicting voter behavior from historical election data. The advantages are that genetic algorithms can solve complex problems where traditional search methods fail, and provide multiple solutions. Limitations include not guaranteeing a global optimum and variable optimization times. Applications include optimization, machine learning, and economic modeling.
P, NP, NP-Complete, and NP-Hard
Reductionism in Algorithms
NP-Completeness and Cooks Theorem
NP-Complete and NP-Hard Problems
Travelling Salesman Problem (TSP)
Travelling Salesman Problem (TSP) - Approximation Algorithms
PRIMES is in P - (A hope for NP problems in P)
Millennium Problems
Conclusions
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.
Genetic Algorithms(GAs) are adaptive heuristic search algorithms that belong to the larger part of evolutionary algorithms. Genetic algorithms are based on the ideas of natural selection and genetics. These are intelligent exploitation of random search provided with historical data to direct the search into the region of better performance in solution space. They are commonly used to generate high-quality solutions for optimization problems and search problems.
Genetic programming is a model of programming that uses biological evolution concepts to solve complex problems. It represents computer programs as parse trees and evolves populations of programs through genetic operations like mutation and crossover. A genetic programming system randomly generates an initial population of programs and uses a fitness function to evaluate them. The fittest programs survive and reproduce new programs to create subsequent generations until a satisfactory solution is found. Genetic programming has been successfully applied to problems like maze navigation, symbolic regression, and neural network optimization.
Genetic algorithms are optimization techniques inspired by biological evolution that can efficiently search large spaces to find optimal solutions; they work by evolving a population of potential solutions through mechanisms like selection, crossover and mutation. Genetic algorithms have been successfully applied to problems in many domains and are now widely used in business, science and engineering for applications like scheduling, design, control, and machine learning.
Evolutionary algorithms are stochastic search and optimization heuristics derived from the classic evolution theory, which are implemented on computers in the majority of cases.
This document provides an overview of fuzzy logic, including its origins, key concepts, and applications. It discusses how fuzzy logic allows for approximate reasoning and decision making under uncertainty by using linguistic variables and fuzzy set theory. Membership functions are used to characterize fuzzy sets and assign degrees of truth between 0 and 1 rather than binary true/false values. Common fuzzy logic operations like intersection, union, and complement are also covered. Finally, some examples of fuzzy logic control systems are presented, including how they are designed using fuzzy rule bases and inference methods like Mamdani and Sugeno.
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.
Nature-Inspired Optimization Algorithms Xin-She Yang
This document discusses nature-inspired optimization algorithms. It begins with an overview of the essence of optimization algorithms and their goal of moving to better solutions. It then discusses some issues with traditional algorithms and how nature-inspired algorithms aim to address these. Several nature-inspired algorithms are described in detail, including particle swarm optimization, firefly algorithm, cuckoo search, and bat algorithm. These are inspired by behaviors in swarms, fireflies, cuckoos, and bats respectively. Examples of applications to engineering design problems are also provided.
This presentation discusses the following ANN concepts:
Introduction
Characteristics
Learning methods
Taxonomy
Evolution of neural networks
Basic models
Important technologies
Applications
Evolutionary Computing is a research area within computer science. As the name suggest, it is a special flavour of computing, which draws inspiration from the process of natural evolution. The fundamental metaphor of evolutionary computing relates this powerful natural evolution to a particular style of problem solving – that of trial and error.
Genetic algorithms are a type of evolutionary algorithm inspired by Darwin's theory of evolution. They use operations like selection, crossover and mutation to evolve solutions to problems over multiple generations. Genetic algorithms work on a population of potential solutions encoded as chromosomes, evolving them toward better solutions. They have been applied to optimization and search problems in various domains like robotics, engineering and bioinformatics.
Soft computing is an approach to computing that aims to model human-like decision making. It deals with imprecise or uncertain data using techniques like fuzzy logic, neural networks, and genetic algorithms. The goal is to develop systems that are tolerant of imprecision, uncertainty, and approximation to achieve practical and low-cost solutions to real-world problems. Soft computing was initiated in 1981 and includes fields like fuzzy logic, neural networks, and evolutionary computation. It provides approximate solutions using techniques like neural network reasoning, genetic programming, and functional approximation.
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.
This document discusses genetic algorithms and their applications. It explains key concepts like genetic crossover, genetic algorithm steps to solve optimization problems, and how genetic algorithms mimic biological evolution. Examples are provided of genetic algorithms being used for tasks like predicting protein structure, automotive design optimization, and generating musical variations. Advantages and limitations of genetic algorithms are also summarized.
The document discusses artificial neural networks and backpropagation. It provides an overview of backpropagation algorithms, including how they were developed over time, the basic methodology of propagating errors backwards, and typical network architectures. It also gives examples of applying backpropagation to problems like robotics, space robots, handwritten digit recognition, and face recognition.
Following topics are discussed in this presentation:What is Soft Computing?
What is Hard Computing?
What is Fuzzy Logic Models?
What is Neural Networks (NN)?
What is Genetic Algorithms or Evaluation Programming?
What is probabilistic reasoning?
Difference between fuzziness and probability
AI and Soft Computing
Future of Soft Computing
Genetic algorithms are inspired by Darwin's theory of natural selection and use techniques like inheritance, mutation, and selection to find optimal solutions. The document discusses genetic algorithms and their application in data mining. It provides examples of how genetic algorithms use selection, crossover, and mutation operators to evolve rules for predicting voter behavior from historical election data. The advantages are that genetic algorithms can solve complex problems where traditional search methods fail, and provide multiple solutions. Limitations include not guaranteeing a global optimum and variable optimization times. Applications include optimization, machine learning, and economic modeling.
P, NP, NP-Complete, and NP-Hard
Reductionism in Algorithms
NP-Completeness and Cooks Theorem
NP-Complete and NP-Hard Problems
Travelling Salesman Problem (TSP)
Travelling Salesman Problem (TSP) - Approximation Algorithms
PRIMES is in P - (A hope for NP problems in P)
Millennium Problems
Conclusions
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.
Genetic Algorithms(GAs) are adaptive heuristic search algorithms that belong to the larger part of evolutionary algorithms. Genetic algorithms are based on the ideas of natural selection and genetics. These are intelligent exploitation of random search provided with historical data to direct the search into the region of better performance in solution space. They are commonly used to generate high-quality solutions for optimization problems and search problems.
Genetic programming is a model of programming that uses biological evolution concepts to solve complex problems. It represents computer programs as parse trees and evolves populations of programs through genetic operations like mutation and crossover. A genetic programming system randomly generates an initial population of programs and uses a fitness function to evaluate them. The fittest programs survive and reproduce new programs to create subsequent generations until a satisfactory solution is found. Genetic programming has been successfully applied to problems like maze navigation, symbolic regression, and neural network optimization.
Genetic algorithms are optimization techniques inspired by biological evolution that can efficiently search large spaces to find optimal solutions; they work by evolving a population of potential solutions through mechanisms like selection, crossover and mutation. Genetic algorithms have been successfully applied to problems in many domains and are now widely used in business, science and engineering for applications like scheduling, design, control, and machine learning.
This document provides an introduction to genetic algorithms including:
1) Genetic algorithms are search and optimization techniques inspired by biological evolution, including mechanisms like reproduction, mutation, and recombination.
2) The key components of a genetic algorithm include encoding a problem as chromosomes, initializing a population of solutions, evaluating fitness, selecting parents, and applying genetic operators to produce new solutions.
3) A genetic algorithm proceeds by generating new populations from the previous generation through the genetic operators until a termination condition is reached.
This document discusses how machines can make decisions using machine learning approaches. It provides an overview of machine learning vocabulary and techniques including supervised learning methods like regression and classification. It also discusses unsupervised learning and examples of clustering emails. The document then demonstrates simple linear and logistic regression models to predict outputs for given inputs. It discusses evaluating models through error measurement and mentions some other machine learning techniques. Finally, it provides an overview of neural networks including feedforward networks and different types like convolutional and recurrent neural networks.
This document discusses how machines can make decisions using machine learning approaches. It provides an overview of machine learning vocabulary and techniques including supervised learning methods like regression and classification. It also discusses unsupervised learning and examples of clustering emails. The document then demonstrates simple linear and logistic regression models to predict outputs given inputs. It discusses evaluating models through error measurement and mentions several other machine learning techniques. Finally, it provides an overview of neural networks including feedforward networks and different types like convolutional and recurrent neural networks.
How Do Gain and Discount Functions Affect the Correlation between DCG and Use...Julián Urbano
We present an empirical analysis of the effect that the gain and discount functions have in the correlation between DCG and user satisfaction. Through a large user study we estimate the relationship between satisfaction and the effectiveness computed with a test collection. In particular, we estimate the probabilities that users find a system satisfactory given a DCG score, and that they agree with a difference in DCG as to which of two systems is more satisfactory. We study this relationship for 36 combinations of gain and discount, and find that a linear gain and a constant discount are best correlated with user satisfaction.
This Presentation discusses about the following topics:
Introduction to Intelligent Systems
Expert Systems
Neural Networks
Fuzzy Logic
Intelligent Agents
Useful Techniques in Artificial IntelligenceIla Group
The document discusses artificial intelligence techniques presented by Dr. Will Browne at Cranfield University. It provides examples of applications of AI techniques in various fields such as finance, industry, engineering and control. It then describes common AI techniques such as expert systems, case-based reasoning, genetic algorithms, neural networks, fuzzy logic and cellular automata. The document emphasizes exploring appropriate techniques for problems and avoiding issues like lack of transparency, garbage in-garbage out, and difficulties generalizing from training data.
Genetic algorithms and traditional algorithms differ in their definitions, usages, and complexity. Genetic algorithms are based on genetics and natural selection, and help find optimal solutions to difficult problems. They are more advanced than traditional algorithms which provide step-by-step procedures. Genetic algorithms are used in fields like machine learning and artificial intelligence, while traditional algorithms are used in programming and mathematics.
This document discusses techniques for testing advanced driver assistance systems (ADAS) through physics-based simulation. It faces challenges due to the large, complex, and multidimensional test input space as well as the computational expense of simulation. The document proposes using a genetic algorithm guided by decision trees to more efficiently search for critical test cases. Classification trees are built to partition the input space into homogeneous regions in order to better guide the selection and generation of test inputs toward more critical areas.
This document discusses defuzzification in fuzzy logic. It defines defuzzification as the process of converting fuzzy quantities into crisp quantities. There are several reasons for and applications of defuzzification, such as converting fuzzy controller outputs into crisp values for applications. The document outlines the defuzzification process and several common defuzzification methods, including the centroid method, weighted average method, and max membership principle. It also discusses the lambda-cut and alpha-cut methods for deriving crisp values from fuzzy sets and relations.
This document provides an introduction to genetic algorithms and their applications in VLSI design and automation. It discusses the fundamentals of genetic algorithms including genetic representation, selection, crossover and mutation operators. Examples are provided for simple function optimization and the traveling salesman problem. The document also discusses applications of genetic algorithms for VLSI design problems such as partitioning, placement, routing, technology mapping and automatic test pattern generation. It provides details on genetic algorithm parameters and compares genetic algorithms to traditional optimization methods.
The data streaming processing paradigm and its use in modern fog architecturesVincenzo Gulisano
Invited lecture at the University of Trieste.
The lecture covers (briefly) the data streaming processing paradigm, research challenges related to distributed, parallel and deterministic streaming analysis and the research of the DCS (Distributed Computing and Systems) groups at Chalmers University of Technology.
Charith Perera, Arkady Zaslavsky, Michael Compton, Peter Christen, and Dimitrios Georgakopoulos, Semantic-driven Configuration of Internet of Things Middleware, Proceedings of the 9th International Conference on Semantics, Knowledge & Grids (SKG), Beijing, China, October, 2013
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.
Machine learning for sensor Data AnalyticsMATLABISRAEL
במצגת זאת נראה כיצד עושים Machine Learning בסביבת MATLAB. נציג מספר יכולות ואפליקציות מובנות ההופכות את תהליך למידת המכונה ליעיל ומהיר יותר – כלים כמו ה-Classification Learner, ה-Regression Learner ו-Bayesian Optimization. בהסתמך על מידע המתקבל מחיישני סמארטפון, נבנה מערכת סיווג המזהה את הפעילות שמבצע המשתמש – הליכה, טיפוס במדרגות, שכיבה, וכו'
This presentation discusses about the following topics:
Hybrid Systems
Hybridization
Combinations
Comparison of Expert Systems, Fuzzy Systems, Neural Networks and Genetic Algorithms
Current Progress
Primary Components
MultiComponents
Degree of Integration
Transformational, hierarchial and integrated
Stand Alone Models
Integrated – Fused Architectures
Generalized Fused Framework
System Types for Hybridization
This document provides an introduction to machine learning, including:
- It discusses how the human brain learns to classify images and how machine learning systems are programmed to perform similar tasks.
- It provides an example of image classification using machine learning and discusses how machines are trained on sample data and then used to classify new queries.
- It outlines some common applications of machine learning in areas like banking, biomedicine, and computer/internet applications. It also discusses popular machine learning algorithms like Bayes networks, artificial neural networks, PCA, SVM classification, and K-means clustering.
Similar to Introduction to Genetic Algorithms (20)
This presentation discusses the following topics:
Basic features of R
Exploring R GUI
Data Frames & Lists
Handling Data in R Workspace
Reading Data Sets & Exporting Data from R
Manipulating & Processing Data in R
Association rule mining is used to find relationships between items in transaction data. It identifies rules that can predict the occurrence of an item based on other items purchased together frequently. Some key metrics used to evaluate rules include support, which measures how frequently an itemset occurs; confidence, which measures how often items in the predicted set occur given items in the predictor set; and lift, which compares the confidence to expected confidence if items were independent. An example association rule evaluated is {Milk, Diaper} -> {Beer} with support of 0.4, confidence of 0.67, and lift of 1.11.
This document discusses clustering, which is the task of grouping data points into clusters so that points within the same cluster are more similar to each other than points in other clusters. It describes different types of clustering methods, including density-based, hierarchical, partitioning, and grid-based methods. It provides examples of specific clustering algorithms like K-means, DBSCAN, and discusses applications of clustering in fields like marketing, biology, libraries, insurance, city planning, and earthquake studies.
Classification is a data analysis technique used to predict class membership for new observations based on a training set of previously labeled examples. It involves building a classification model during a training phase using an algorithm, then testing the model on new data to estimate accuracy. Some common classification algorithms include decision trees, Bayesian networks, neural networks, and support vector machines. Classification has applications in domains like medicine, retail, and entertainment.
The document discusses the assumptions and properties of ordinary least squares (OLS) estimators in linear regression analysis. It notes that OLS estimators are best linear unbiased estimators (BLUE) if the assumptions of the linear regression model are met. Specifically, it assumes errors have zero mean and constant variance, are uncorrelated, and are normally distributed. Violation of the assumption of constant variance is known as heteroscedasticity. The document outlines how heteroscedasticity impacts the properties of OLS estimators and their use in applications like econometrics.
This document provides an introduction to regression analysis. It discusses that regression analysis investigates the relationship between dependent and independent variables to model and analyze data. The document outlines different types of regressions including linear, polynomial, stepwise, ridge, lasso, and elastic net regressions. It explains that regression analysis is used for predictive modeling, forecasting, and determining the impact of variables. The benefits of regression analysis are that it indicates significant relationships and the strength of impact between variables.
MYCIN was an early expert system developed at Stanford University in 1972 to assist physicians in diagnosing and selecting treatment for bacterial and blood infections. It used over 600 production rules encoding the clinical decision criteria of infectious disease experts to diagnose patients based on reported symptoms and test results. While it could not replace human diagnosis due to computing limitations at the time, MYCIN demonstrated that expert knowledge could be represented computationally and established a foundation for more advanced machine learning and knowledge base systems.
The document discusses expert systems, which are computer applications that solve complex problems at a human expert level. It describes the characteristics and capabilities of expert systems, why they are useful, and their key components - knowledge base, inference engine, and user interface. The document also outlines common applications of expert systems and the general development process.
The Dempster-Shafer Theory was developed by Arthur Dempster in 1967 and Glenn Shafer in 1976 as an alternative to Bayesian probability. It allows one to combine evidence from different sources and obtain a degree of belief (or probability) for some event. The theory uses belief functions and plausibility functions to represent degrees of belief for various hypotheses given certain evidence. It was developed to describe ignorance and consider all possible outcomes, unlike Bayesian probability which only considers single evidence. An example is given of using the theory to determine the murderer in a room with 4 people where the lights went out.
A Bayesian network is a probabilistic graphical model that represents conditional dependencies among random variables using a directed acyclic graph. It consists of nodes representing variables and directed edges representing causal relationships. Each node contains a conditional probability table that quantifies the effect of its parent nodes on that variable. Bayesian networks can be used to calculate the probability of events occurring based on the network structure and conditional probability tables, such as computing the probability of an alarm sounding given that no burglary or earthquake occurred but two neighbors called.
This document discusses knowledge-based agents in artificial intelligence. It defines knowledge-based agents as agents that maintain an internal state of knowledge, reason over that knowledge, update their knowledge based on observations, and take actions. Knowledge-based agents have two main components: a knowledge base that stores facts about the world, and an inference system that applies logical rules to deduce new information from the knowledge base. The document also describes the architecture of knowledge-based agents and different approaches to designing them.
A rule-based system uses predefined rules to make logical deductions and choices to perform automated actions. It consists of a database of rules representing knowledge, a database of facts as inputs, and an inference engine that controls the process of deriving conclusions by applying rules to facts. A rule-based system mimics human decision making by applying rules in an "if-then" format to incoming data to perform actions, but unlike AI it does not learn or adapt on its own.
This document discusses formal logic and its applications in AI and machine learning. It begins by explaining why logic is useful in complex domains or with little data. It then describes logic-based approaches to AI that use symbolic reasoning as an alternative to machine learning. The document proceeds to explain propositional logic and first-order logic, noting how first-order logic improves on propositional logic by allowing variables. It also mentions other logics and their applications in areas like automated discovery, inductive programming, and verification of computer systems and machine learning models.
The document discusses production systems, which are rule-based systems used in artificial intelligence to model intelligent behavior. A production system consists of a global database, set of production rules, and control system. The rules fire to modify the database based on conditions. Different control strategies are used to determine which rules fire. Production systems are modular and allow knowledge representation as condition-action rules. Examples of applications in problem solving are provided.
The document discusses game playing in artificial intelligence. It describes how general game playing (GGP) involves designing AI that can play multiple games by learning the rules, rather than being programmed for a specific game. The document outlines how the minimax algorithm is commonly used for game playing, involving move generation and static evaluation functions to search game trees and determine the best move by maximizing or minimizing values at each level.
A study on “Diagnosis Test of Diabetics and Hypertension by AI”, Presentation slides for International Conference on "Life Sciences: Acceptance of the New Normal", St. Aloysius' College, Jabalpur, Madhya Pradesh, India, 27-28 August, 2021
A study on “impact of artificial intelligence in covid19 diagnosis”Dr. C.V. Suresh Babu
A study on “Impact of Artificial Intelligence in COVID-19 Diagnosis”, Presentation slides for International Conference on "Life Sciences: Acceptance of the New Normal", St. Aloysius' College, Jabalpur, Madhya Pradesh, India, 27-28 August, 2021
A study on “impact of artificial intelligence in covid19 diagnosis”Dr. C.V. Suresh Babu
Although the lungs are one of the most vital organs in the body, they are vulnerable to infection and injury. COVID-19 has put the entire world in an unprecedented difficult situation, bringing life to a halt and claiming thousands of lives all across the world. Medical imaging, such as X-rays and computed tomography (CT), is essential in the global fight against COVID-19, and newly emerging artificial intelligence (AI) technologies are boosting the power of imaging tools and assisting medical specialists. AI can improve job efficiency by precisely identifying infections in X-ray and CT images and allowing further measurement. We focus on the integration of AI with X-ray and CT, both of which are routinely used in frontline hospitals, to reflect the most recent progress in medical imaging and radiology combating COVID-19.
Gender and Mental Health - Counselling and Family Therapy Applications and In...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
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واخيراً هذهِ الملزمة حلالٌ عليكم وإتمنى منكم إن تدعولي بالخير والصحة والعافية فقط
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1. Department of Information Technology 1Soft Computing (ITC4256 )
Dr. C.V. Suresh Babu
Professor
Department of IT
Hindustan Institute of Science & Technology
Introduction to Genetic Algorithms
2. Department of Information Technology 2Soft Computing (ITC4256 )
Action Plan
• What is Genetic Algorithms?
• Introduction to Genetic Algorithm
• Classes of Search Techniques
• Components of a GA
• Components of a GA
• Simple Genetic Algorithm
• GA Cycle of Reproduction
• Population
• Reproduction
• Chromosome Modification: Mutation, Crossover, Evaluation, Deletion
• Example
• GA Technology
• Issues for GA Practitioners
• Benefits of Genetic Algorithms
• GA Application Types
• Quiz
3. Department of Information Technology 3Soft Computing (ITC4256 )
“Genetic Algorithms are
good at taking large,
potentially huge search
spaces and navigating
them, looking for optimal
combinations of things,
solutions you might not
otherwise find in a
lifetime.”
- Salvatore Mangano
Computer Design, May 1995
What is Genetic Algorithms?
4. Department of Information Technology 4Soft Computing (ITC4256 )
Introduction to Genetic Algorithm
• Directed search algorithms based on the mechanics of
biological evolution
• Developed by John Holland, University of Michigan (1970’s)
– To understand the adaptive processes of natural systems
– To design artificial systems software that retains the robustness of
natural systems
5. Department of Information Technology 5Soft Computing (ITC4256 )
Introduction Genetic Algorithm (cont.)
• Provide efficient, effective techniques for optimization and
machine learning applications
• Widely-used today in business, scientific and engineering
circles
6. Department of Information Technology 6Soft Computing (ITC4256 )
Classes of Search Techniques
Finonacci Newton
Direct methods Indirect methods
Calculus-based techniques
Evolutionary strategies
Centralized Distributed
Parallel
Steady-state Generational
Sequential
Genetic algorithms
Evolutionary algorithms Simulated annealing
Guided random search techniques
Dynamic programming
Enumerative techniques
Search techniques
7. Department of Information Technology 7Soft Computing (ITC4256 )
Components of a GA
A problem to solve, and ...
• Encoding technique (gene, chromosome)
• Initialization procedure (creation)
• Evaluation function (environment)
• Selection of parents (reproduction)
• Genetic operators (mutation, recombination)
• Parameter settings (practice and art)
8. Department of Information Technology 8Soft Computing (ITC4256 )
Simple Genetic Algorithm
{
initialize population;
evaluate population;
while TerminationCriteriaNotSatisfied
{
select parents for reproduction;
perform recombination and mutation;
evaluate population;
}
}
9. Department of Information Technology 9Soft Computing (ITC4256 )
The GA Cycle of Reproduction
reproduction
population evaluation
modification
discard
deleted
members
parents
children
modified
children
evaluated children
10. Department of Information Technology 10Soft Computing (ITC4256 )
Population
Chromosomes could be:
– Bit strings (0101 ... 1100)
– Real numbers (43.2 -33.1 ... 0.0 89.2)
– Permutations of element (E11 E3 E7 ... E1 E15)
– Lists of rules (R1 R2 R3 ... R22 R23)
– Program elements (genetic programming)
– ... any data structure ...
population
11. Department of Information Technology 11Soft Computing (ITC4256 )
Reproduction
reproduction
population
parents
children
Parents are selected at random with
selection chances biased in relation to
chromosome evaluations.
12. Department of Information Technology 12Soft Computing (ITC4256 )
Chromosome Modification
modification
children
• Modifications are stochastically triggered
• Operator types are:
– Mutation
– Crossover (recombination)
modified children
13. Department of Information Technology 13Soft Computing (ITC4256 )
Mutation: Local Modification
Before: (1 0 1 1 0 1 1 0)
After: (0 1 1 0 0 1 1 0)
Before: (1.38 -69.4 326.44 0.1)
After: (1.38 -67.5 326.44 0.1)
• Causes movement in the search space
(local or global)
• Restores lost information to the population
14. Department of Information Technology 14Soft Computing (ITC4256 )
Crossover: Recombination
P1 (0 1 1 0 1 0 0 0) (0 1 0 0 1 0 0 0) C1
P2 (1 1 0 1 1 0 1 0) (1 1 1 1 1 0 1 0) C2
Crossover is a critical feature of genetic
algorithms:
– It greatly accelerates search early in evolution of a population
– It leads to effective combination of schemata (subsolutions on
different chromosomes)
*
15. Department of Information Technology 15Soft Computing (ITC4256 )
Evaluation
• The evaluator decodes a chromosome and assigns it a fitness measure
• The evaluator is the only link between a classical GA and the problem it
is solving
evaluation
evaluated
children
modified
children
16. Department of Information Technology 16Soft Computing (ITC4256 )
Deletion
• Generational GA:
entire populations replaced with each iteration
• Steady-state GA:
a few members replaced each generation
population
discard
discarded members
17. Department of Information Technology 17Soft Computing (ITC4256 )
An Abstract Example
Distribution of Individuals in Generation 0
Distribution of Individuals in Generation N
18. Department of Information Technology 18Soft Computing (ITC4256 )
A Simple Example
“The Gene is by far the most sophisticated program around.”
- Bill Gates, Business Week, June 27, 1994
19. Department of Information Technology 19Soft Computing (ITC4256 )
A Simple Example
The Traveling Salesman Problem:
Find a tour of a given set of cities so that
– each city is visited only once
– the total distance traveled is minimized
20. Department of Information Technology 20Soft Computing (ITC4256 )
Representation
Representation is an ordered list of city
numbers known as an order-based GA.
1) London 3) Dunedin 5) Beijing 7) Tokyo
2) Venice 4) Singapore 6) Phoenix 8) Victoria
CityList1 (3 5 7 2 1 6 4 8)
CityList2 (2 5 7 6 8 1 3 4)
21. Department of Information Technology 21Soft Computing (ITC4256 )
Crossover
Crossover combines inversion and
recombination:
* *
Parent1 (3 5 7 2 1 6 4 8)
Parent2 (2 5 7 6 8 1 3 4)
Child (2 5 7 2 1 6 3 4)
This operator is called the Order1 crossover.
22. Department of Information Technology 22Soft Computing (ITC4256 )
Mutation involves reordering of the list:
* *
Before: (5 8 7 2 1 6 3 4)
After: (5 8 6 2 1 7 3 4)
Mutation
23. Department of Information Technology 23Soft Computing (ITC4256 )
TSP Example: 30 Cities
0
20
40
60
80
100
120
0 10 20 30 40 50 60 70 80 90 100
y
x
24. Department of Information Technology 24Soft Computing (ITC4256 )
Solution i (Distance = 941)
0
20
40
60
80
100
120
0 10 20 30 40 50 60 70 80 90 100
y
x
TSP30 (Performance = 941)
28. Department of Information Technology 28Soft Computing (ITC4256 )
Overview of Performance
0
200
400
600
800
1000
1200
1400
1600
1800
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
D
is
ta
n
c
e
Generations (1000)
TSP30 - Overview of Performance
Best
Worst
Average
29. Department of Information Technology 29Soft Computing (ITC4256 )
Considering the GA Technology
“Almost eight years ago ...
people at Microsoft wrote
a program [that] uses
some genetic things for
finding short code
sequences. Windows 2.0
and 3.2, NT, and almost all
Microsoft applications
products have shipped
with pieces of code created
by that system.”
- Nathan Myhrvold, Microsoft Advanced
Technology Group, Wired, September 1995
30. Department of Information Technology 30Soft Computing (ITC4256 )
Issues for GA Practitioners
• Choosing basic implementation issues:
– representation
– population size, mutation rate, ...
– selection, deletion policies
– crossover, mutation operators
• Termination Criteria
• Performance, scalability
• Solution is only as good as the evaluation function (often hardest part)
31. Department of Information Technology 31Soft Computing (ITC4256 )
Benefits of Genetic Algorithms
• Concept is easy to understand
• Modular, separate from application
• Supports multi-objective optimization
• Good for “noisy” environments
• Always an answer; answer gets better with time
• Inherently parallel; easily distributed
32. Department of Information Technology 32Soft Computing (ITC4256 )
Benefits of Genetic Algorithms (cont.)
• Many ways to speed up and improve a GA-based application as
knowledge about problem domain is gained
• Easy to exploit previous or alternate solutions
• Flexible building blocks for hybrid applications
• Substantial history and range of use
33. Department of Information Technology 33Soft Computing (ITC4256 )
When to Use a GA
• Alternate solutions are too slow or overly complicated
• Need an exploratory tool to examine new approaches
• Problem is similar to one that has already been successfully solved by using a
GA
• Want to hybridize with an existing solution
• Benefits of the GA technology meet key problem requirements
34. Department of Information Technology 34Soft Computing (ITC4256 )
Some GA Application Types
Domain Application Types
Control gas pipeline, pole balancing, missile evasion, pursuit
Design semiconductor layout, aircraft design, keyboard
configuration, communication networks
Scheduling manufacturing, facility scheduling, resource allocation
Robotics trajectory planning
Machine Learning designing neural networks, improving classification
algorithms, classifier systems
Signal Processing filter design
Game Playing poker, checkers, prisoner’s dilemma
Combinatorial
Optimization
set covering, travelling salesman, routing, bin packing,
graph colouring and partitioning
35. Department of Information Technology 35Soft Computing (ITC4256 )
Conclusions
Question: ‘If GAs are so smart, why ain’t they rich?’
Answer: ‘Genetic algorithms are rich - rich in
application across a large and growing
number of disciplines.’
- David E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning
36. Department of Information Technology 36Soft Computing (ITC4256 )
Test Yourself
1. Genetic Algorithm are a part of
a. Evolutionary Computing
b. inspired by Darwin's theory about evolution - "survival of the fittest"
c. are adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetics
d. All of the above
2. Which of the following are discrete optimization problems?
a. Travelling salesman problem
b. Robot control
c. Chess playing program
d. Prediction of stock prices
3. Biologically inspired computations appropriate for
a. Optimization
b. Modelling
c. Safety critical systems
d. Simulation
4. Exploration in search is
a. Concerned with improving the current best solution by local search
b. Combined with exploitation in evolutionary algorithms
c. Often resulting in getting stuck in local optima
d. Concerned with global search
5. Evolutionary algorithm :Initialization
a. Individuals are normally generated randomly
b. Is concerned with generating candidate solutions
c. Mutation of candidates is normally also taking place during the initialization
d. Heuristics for generating candidates can be applied
37. Department of Information Technology 37Soft Computing (ITC4256 )
Answers
1. Genetic Algorithm are a part of
a. Evolutionary Computing
b. inspired by Darwin's theory about evolution - "survival of the fittest"
c. are adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetics
d. All of the above
2. Which of the following are discrete optimization problems?
a. Travelling salesman problem
b. Robot control
c. Chess playing program
d. Prediction of stock prices
3. Biologically inspired computations appropriate for
a. Optimization
b. Modelling
c. Safety critical systems
d. Simulation
4. Exploration in search is
a. Concerned with improving the current best solution by local search
b. Combined with exploitation in evolutionary algorithms
c. Often resulting in getting stuck in local optima
d. Concerned with global search
5. Evolutionary algorithm :Initialization
a. Individuals are normally generated randomly
b. Is concerned with generating candidate solutions
c. Mutation of candidates is normally also taking place during the initialization
d. Heuristics for generating candidates can be applied