This document describes genetic algorithms and provides an example of how one works. It defines genetic algorithms as evolutionary algorithms that use techniques inspired by evolutionary biology like inheritance, mutation, selection, and crossover. The document then outlines the typical components of a genetic algorithm, including initialization of a random population, fitness evaluation, selection of parents, crossover and mutation to produce offspring, and iteration until a termination condition is met. It concludes by showing pseudocode for a genetic algorithm to solve the onemax problem and output from running the algorithm.
Genetic algorithms are a type of evolutionary algorithm that mimics natural selection. They operate on a population of potential solutions applying operators like selection, crossover and mutation to produce the next generation. The algorithm iterates until a termination condition is met, such as a solution being found or a maximum number of generations being produced. Genetic algorithms are useful for optimization and search problems as they can handle large, complex search spaces. However, they require properly defining the fitness function and tuning various parameters like population size, mutation rate and crossover rate.
This document provides an introduction to genetic algorithms. It describes genetic algorithms as probabilistic optimization algorithms inspired by biological evolution, using concepts like natural selection and genetic inheritance. The key components of a genetic algorithm are described, including encoding solutions, initializing a population, selecting parents, applying genetic operators like crossover and mutation, evaluating fitness, and establishing termination criteria. An example problem of maximizing binary string ones is used to illustrate how a genetic algorithm works over multiple generations.
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.
Genetic algorithms are heuristic search methods inspired by natural selection that can be used to find optimized solutions to problems. They work by generating an initial random population of solutions and then applying genetic operations like selection, crossover and mutation to produce new solutions over multiple generations. The fittest solutions survive and weaker ones die out, causing the overall population to become better adapted to the problem being solved. Genetic algorithms are well-suited for searching large, complex datasets and problems with multimodal or n-dimensional search spaces.
This document discusses genetic algorithms and their components. It begins by explaining that genetic algorithms are a type of evolutionary algorithm inspired by biological evolution that uses techniques like inheritance, mutation, selection, and crossover. It then defines the key terms used in genetic algorithms, such as individuals, populations, chromosomes, genes, and fitness functions. The rest of the document provides more details on genetic algorithm components like representation of solutions, selection of individuals, crossover and mutation operations, and the general genetic algorithm process.
Genetic algorithm (ga) binary and real Vijay Bhaskar SemwalIIIT Allahabad
The document describes the process of genetic algorithms using a binary string representation example. It explains the initial population is generated randomly, fitness scores are assigned, and selection, crossover, and mutation are applied to produce the next generation. The roulette wheel selection method is used to probabilistically select individuals for reproduction based on their fitness. Two point crossover and bit mutation are the genetic operators applied, and the process repeats until a stopping criterion is met. Schemata and their order are also introduced to analyze the survival probability of building blocks under genetic operations.
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 provides an overview of their key concepts and components. It describes how genetic algorithms are inspired by Darwinian evolution and use techniques like selection, crossover and mutation to evolve solutions to optimization problems. It also outlines various parameters and strategies used in genetic algorithms, including chromosome representation, population size, selection methods, and termination criteria. A wide range of applications are mentioned where genetic algorithms have been applied successfully.
Genetic algorithms are a type of evolutionary algorithm that mimics natural selection. They operate on a population of potential solutions applying operators like selection, crossover and mutation to produce the next generation. The algorithm iterates until a termination condition is met, such as a solution being found or a maximum number of generations being produced. Genetic algorithms are useful for optimization and search problems as they can handle large, complex search spaces. However, they require properly defining the fitness function and tuning various parameters like population size, mutation rate and crossover rate.
This document provides an introduction to genetic algorithms. It describes genetic algorithms as probabilistic optimization algorithms inspired by biological evolution, using concepts like natural selection and genetic inheritance. The key components of a genetic algorithm are described, including encoding solutions, initializing a population, selecting parents, applying genetic operators like crossover and mutation, evaluating fitness, and establishing termination criteria. An example problem of maximizing binary string ones is used to illustrate how a genetic algorithm works over multiple generations.
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.
Genetic algorithms are heuristic search methods inspired by natural selection that can be used to find optimized solutions to problems. They work by generating an initial random population of solutions and then applying genetic operations like selection, crossover and mutation to produce new solutions over multiple generations. The fittest solutions survive and weaker ones die out, causing the overall population to become better adapted to the problem being solved. Genetic algorithms are well-suited for searching large, complex datasets and problems with multimodal or n-dimensional search spaces.
This document discusses genetic algorithms and their components. It begins by explaining that genetic algorithms are a type of evolutionary algorithm inspired by biological evolution that uses techniques like inheritance, mutation, selection, and crossover. It then defines the key terms used in genetic algorithms, such as individuals, populations, chromosomes, genes, and fitness functions. The rest of the document provides more details on genetic algorithm components like representation of solutions, selection of individuals, crossover and mutation operations, and the general genetic algorithm process.
Genetic algorithm (ga) binary and real Vijay Bhaskar SemwalIIIT Allahabad
The document describes the process of genetic algorithms using a binary string representation example. It explains the initial population is generated randomly, fitness scores are assigned, and selection, crossover, and mutation are applied to produce the next generation. The roulette wheel selection method is used to probabilistically select individuals for reproduction based on their fitness. Two point crossover and bit mutation are the genetic operators applied, and the process repeats until a stopping criterion is met. Schemata and their order are also introduced to analyze the survival probability of building blocks under genetic operations.
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 provides an overview of their key concepts and components. It describes how genetic algorithms are inspired by Darwinian evolution and use techniques like selection, crossover and mutation to evolve solutions to optimization problems. It also outlines various parameters and strategies used in genetic algorithms, including chromosome representation, population size, selection methods, and termination criteria. A wide range of applications are mentioned where genetic algorithms have been applied successfully.
This document provides an overview of genetic algorithms (GAs). It describes Holland's simple genetic algorithm (SGA) model including representation, selection, crossover and mutation operators. Real-valued and permutation representations are discussed along with associated operators. Alternative population models and selection mechanisms are also summarized.
The genetic algorithm is a mataheuristic method that uses the metaphor of the evolutionary process of living things, especially Darwin's theory of evolution. This persentation will discuss about the fundamental of Genetic Algorithm. Download this PPT and put in "Slide Persentation (F5)" to play the animation in it.
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 discusses advanced optimization techniques used to solve large-scale problems that traditional techniques cannot handle effectively. It introduces several population-based metaheuristic algorithms inspired by natural phenomena, including genetic algorithms, artificial immune algorithms, and differential evolution. Genetic algorithms use operations like selection, crossover and mutation to evolve solutions over generations. Artificial immune algorithms are based on clonal selection to amplify high-affinity antibodies. Differential evolution generates trial vectors through mutation and crossover of randomly selected target vectors.
This document summarizes evolutionary computation techniques including genetic algorithms and genetic programming. It provides an overview of biological evolution and how evolutionary computation mimics this process to solve problems. Genetic algorithms use chromosomes to represent candidate solutions which are evolved over generations using selection, crossover and mutation operators. Genetic programming uses tree representations to evolve computer programs. The document describes how genetic programming can be used to evolve a program for a wall-following robot. It concludes by discussing applications and advantages/disadvantages of evolutionary computation.
Genetic algorithms (GAs) are optimization algorithms inspired by Darwinian evolution. They use techniques like mutation, crossover, and selection to evolve solutions to problems iteratively. The document provides examples to illustrate how GAs work, including finding a binary number and fitting a polynomial to data points. GAs initialize a population of random solutions, then improve it over generations by keeping the fittest solutions and breeding them using crossover and mutation to produce new solutions, until finding an optimal or near-optimal solution.
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.
The document provides an overview of evolutionary algorithms (EAs), including how they are population-based algorithms inspired by Darwinian natural selection. EAs operate on a population of potential solutions, applying the principle of survival of the fittest to produce better approximations over generations. Key characteristics of EAs include representation of solutions, selection of parents for mating, recombination to combine parents' genes, mutation of genes, a fitness function to evaluate solutions, and survivor selection. The document also discusses different types of EAs and their characteristics.
Solving non linear programming minimization problem using genetic algorithmLahiru Dilshan
This document describes solving a non-linear programming minimization problem using a genetic algorithm. It discusses:
1) How genetic algorithms work and how they are applied to solve optimization problems.
2) The steps of the genetic algorithm used: initializing the population randomly, decoding genotypes to phenotypes, evaluating fitness, selecting parents, performing crossover and mutation to generate offspring, and selecting the next generation.
3) The specific non-linear problem to be solved, including defining the objective function and constraints. The genetic algorithm parameters and programming concepts used to find the minimum are also described.
A Review On Genetic Algorithm And Its ApplicationsKaren Gomez
This document provides an overview of genetic algorithms and their applications. It begins with an introduction to genetic algorithms, explaining that they are inspired by Darwin's theory of evolution and use techniques like mutation and crossover to evolve solutions to problems. The document then covers biological concepts related to genetics like chromosomes, genes, alleles, and reproduction. It discusses how genetic algorithms represent potential solutions as chromosomes and use selection, crossover and mutation operators to evolve new solutions. The document also covers genetic algorithm parameters and applications to problems like the traveling salesman problem.
Genetic algorithms are a general purpose learning algorithm that mimic the theory of evolution and natural selection. They can be used to solve complex problems by evolving solutions over multiple generations using techniques inspired by natural selection, including selection, crossover and mutation. Genetic algorithms represent potential solutions as chromosomes and evaluate them using a fitness function to determine how well they solve the problem. New solutions are created by selecting the fittest parents and breeding them using crossover and mutation operators until an optimal solution is found or a stopping criteria is reached.
Genetic algorithms are search algorithms inspired by biological evolution that use techniques like mutation, crossover, and selection to evolve solutions to problems. They represent potential solutions as individuals in a population and evolve the population over multiple generations using genetic operators to improve the overall quality of solutions. Genetic programming is a type of genetic algorithm that evolves computer programs to solve problems by genetically breeding populations of computer programs.
Traveling Salesman Problem (TSP) is a kind of NPHard problem which cant be solved in polynomial time for
asymptotically large values of n. In this paper a balanced combination of Genetic algorithm and Simulated Annealing is used. To
improve the performance of finding optimal solution from huge
search space, we have incorporated the use of tournament and
rank as selection operator. And Inver-over operator Mechanism
for crossover and mutation . To illustrate it more clearly an
implementation in C++ (4.9.9.2) has been done.
Index Terms—Genetic Algorithm (GA) , Simulated Annealing
(SA) , Inver-over operator , Lin-Kernighan algorithm , selection
operator , crossover operator , mutation operator.
Data Science - Part XIV - Genetic AlgorithmsDerek Kane
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.
Genetic algorithms are a type of evolutionary algorithm that uses techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover. They are implemented as computer simulations that evolve solutions to optimization and search problems. Genetic algorithms use a population of abstract representations of candidate solutions called chromosomes. Operators like crossover and mutation are applied to chromosomes to generate new populations, with the fittest solutions most likely to reproduce and pass on their traits to the next generation. This process is repeated until a satisfactory solution is found.
Genetic algorithms are a type of evolutionary algorithm that use techniques inspired by Darwinian evolution such as inheritance, mutation, selection, and crossover. They are commonly used to find optimal or near-optimal solutions to difficult problems by mimicking natural selection. A genetic algorithm begins with a population of random solutions and uses selection, crossover, and mutation to generate new solutions. The fittest solutions survive and are selected to reproduce, creating a new generation. This process is repeated until a termination condition is met. Genetic algorithms are inspired by biological evolution and can be applied to optimization and search problems.
Genetic algorithms are a type of evolutionary algorithm that use techniques inspired by Darwinian evolution such as inheritance, mutation, selection, and crossover. They are commonly used to find optimal or near-optimal solutions to difficult problems by mimicking natural selection. A genetic algorithm begins with a population of random solutions and uses selection, crossover, and mutation to generate new solutions. The fittest solutions survive and less fit solutions are removed. This process is repeated until an optimal solution is found.
Genetic algorithms are a type of evolutionary algorithm that use techniques inspired by Darwinian evolution such as inheritance, mutation, selection, and crossover. They are commonly used to find optimal or near-optimal solutions to difficult problems by mimicking natural selection. A genetic algorithm initializes a population of random solutions and uses selection, crossover, and mutation to generate new solutions. The fittest solutions survive to be selected for the next generation. This process is repeated until a termination condition is reached. Genetic algorithms are inspired by biological evolution and can be applied to optimization and search problems.
Genetic algorithms are a type of evolutionary algorithm that use techniques inspired by Darwinian evolution, such as inheritance, mutation, selection, and crossover. They are commonly used to generate useful solutions to optimization and search problems by evolving candidate solutions over generations. Genetic algorithms work on a population of candidate solutions represented by chromosomes. They evolve toward better solutions through techniques like selection of the fittest solutions, crossover of parent solutions to create new solutions, and random mutation of new solutions. The algorithm terminates when either a maximum number of generations has been produced or a satisfactory fitness level has been reached in the population.
This document discusses classification and decision trees. It defines classification as predicting categorical labels, and lists some common classification methods and examples. It then defines key concepts like attributes, datasets, entropy, information gain, and decision tree components. It provides exercises to calculate entropy and information gain from sample datasets and contingency tables.
This chapter discusses modeling decision processes and decision support systems. It covers the typical modeling process of identifying a problem, analyzing requirements, and identifying variables and relationships. Common error in problem definition is premature focus on solutions rather than fully defining the problem. Tools for structuring problems include influence diagrams and decision trees. Probability can be estimated through frequency, subjectively, or through logic. Techniques for forecasting probabilities include direct estimation, odds forecasting, and comparison forecasting. Sensitivity analysis and value analysis are also discussed.
This document provides an overview of genetic algorithms (GAs). It describes Holland's simple genetic algorithm (SGA) model including representation, selection, crossover and mutation operators. Real-valued and permutation representations are discussed along with associated operators. Alternative population models and selection mechanisms are also summarized.
The genetic algorithm is a mataheuristic method that uses the metaphor of the evolutionary process of living things, especially Darwin's theory of evolution. This persentation will discuss about the fundamental of Genetic Algorithm. Download this PPT and put in "Slide Persentation (F5)" to play the animation in it.
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 discusses advanced optimization techniques used to solve large-scale problems that traditional techniques cannot handle effectively. It introduces several population-based metaheuristic algorithms inspired by natural phenomena, including genetic algorithms, artificial immune algorithms, and differential evolution. Genetic algorithms use operations like selection, crossover and mutation to evolve solutions over generations. Artificial immune algorithms are based on clonal selection to amplify high-affinity antibodies. Differential evolution generates trial vectors through mutation and crossover of randomly selected target vectors.
This document summarizes evolutionary computation techniques including genetic algorithms and genetic programming. It provides an overview of biological evolution and how evolutionary computation mimics this process to solve problems. Genetic algorithms use chromosomes to represent candidate solutions which are evolved over generations using selection, crossover and mutation operators. Genetic programming uses tree representations to evolve computer programs. The document describes how genetic programming can be used to evolve a program for a wall-following robot. It concludes by discussing applications and advantages/disadvantages of evolutionary computation.
Genetic algorithms (GAs) are optimization algorithms inspired by Darwinian evolution. They use techniques like mutation, crossover, and selection to evolve solutions to problems iteratively. The document provides examples to illustrate how GAs work, including finding a binary number and fitting a polynomial to data points. GAs initialize a population of random solutions, then improve it over generations by keeping the fittest solutions and breeding them using crossover and mutation to produce new solutions, until finding an optimal or near-optimal solution.
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.
The document provides an overview of evolutionary algorithms (EAs), including how they are population-based algorithms inspired by Darwinian natural selection. EAs operate on a population of potential solutions, applying the principle of survival of the fittest to produce better approximations over generations. Key characteristics of EAs include representation of solutions, selection of parents for mating, recombination to combine parents' genes, mutation of genes, a fitness function to evaluate solutions, and survivor selection. The document also discusses different types of EAs and their characteristics.
Solving non linear programming minimization problem using genetic algorithmLahiru Dilshan
This document describes solving a non-linear programming minimization problem using a genetic algorithm. It discusses:
1) How genetic algorithms work and how they are applied to solve optimization problems.
2) The steps of the genetic algorithm used: initializing the population randomly, decoding genotypes to phenotypes, evaluating fitness, selecting parents, performing crossover and mutation to generate offspring, and selecting the next generation.
3) The specific non-linear problem to be solved, including defining the objective function and constraints. The genetic algorithm parameters and programming concepts used to find the minimum are also described.
A Review On Genetic Algorithm And Its ApplicationsKaren Gomez
This document provides an overview of genetic algorithms and their applications. It begins with an introduction to genetic algorithms, explaining that they are inspired by Darwin's theory of evolution and use techniques like mutation and crossover to evolve solutions to problems. The document then covers biological concepts related to genetics like chromosomes, genes, alleles, and reproduction. It discusses how genetic algorithms represent potential solutions as chromosomes and use selection, crossover and mutation operators to evolve new solutions. The document also covers genetic algorithm parameters and applications to problems like the traveling salesman problem.
Genetic algorithms are a general purpose learning algorithm that mimic the theory of evolution and natural selection. They can be used to solve complex problems by evolving solutions over multiple generations using techniques inspired by natural selection, including selection, crossover and mutation. Genetic algorithms represent potential solutions as chromosomes and evaluate them using a fitness function to determine how well they solve the problem. New solutions are created by selecting the fittest parents and breeding them using crossover and mutation operators until an optimal solution is found or a stopping criteria is reached.
Genetic algorithms are search algorithms inspired by biological evolution that use techniques like mutation, crossover, and selection to evolve solutions to problems. They represent potential solutions as individuals in a population and evolve the population over multiple generations using genetic operators to improve the overall quality of solutions. Genetic programming is a type of genetic algorithm that evolves computer programs to solve problems by genetically breeding populations of computer programs.
Traveling Salesman Problem (TSP) is a kind of NPHard problem which cant be solved in polynomial time for
asymptotically large values of n. In this paper a balanced combination of Genetic algorithm and Simulated Annealing is used. To
improve the performance of finding optimal solution from huge
search space, we have incorporated the use of tournament and
rank as selection operator. And Inver-over operator Mechanism
for crossover and mutation . To illustrate it more clearly an
implementation in C++ (4.9.9.2) has been done.
Index Terms—Genetic Algorithm (GA) , Simulated Annealing
(SA) , Inver-over operator , Lin-Kernighan algorithm , selection
operator , crossover operator , mutation operator.
Data Science - Part XIV - Genetic AlgorithmsDerek Kane
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.
Genetic algorithms are a type of evolutionary algorithm that uses techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover. They are implemented as computer simulations that evolve solutions to optimization and search problems. Genetic algorithms use a population of abstract representations of candidate solutions called chromosomes. Operators like crossover and mutation are applied to chromosomes to generate new populations, with the fittest solutions most likely to reproduce and pass on their traits to the next generation. This process is repeated until a satisfactory solution is found.
Genetic algorithms are a type of evolutionary algorithm that use techniques inspired by Darwinian evolution such as inheritance, mutation, selection, and crossover. They are commonly used to find optimal or near-optimal solutions to difficult problems by mimicking natural selection. A genetic algorithm begins with a population of random solutions and uses selection, crossover, and mutation to generate new solutions. The fittest solutions survive and are selected to reproduce, creating a new generation. This process is repeated until a termination condition is met. Genetic algorithms are inspired by biological evolution and can be applied to optimization and search problems.
Genetic algorithms are a type of evolutionary algorithm that use techniques inspired by Darwinian evolution such as inheritance, mutation, selection, and crossover. They are commonly used to find optimal or near-optimal solutions to difficult problems by mimicking natural selection. A genetic algorithm begins with a population of random solutions and uses selection, crossover, and mutation to generate new solutions. The fittest solutions survive and less fit solutions are removed. This process is repeated until an optimal solution is found.
Genetic algorithms are a type of evolutionary algorithm that use techniques inspired by Darwinian evolution such as inheritance, mutation, selection, and crossover. They are commonly used to find optimal or near-optimal solutions to difficult problems by mimicking natural selection. A genetic algorithm initializes a population of random solutions and uses selection, crossover, and mutation to generate new solutions. The fittest solutions survive to be selected for the next generation. This process is repeated until a termination condition is reached. Genetic algorithms are inspired by biological evolution and can be applied to optimization and search problems.
Genetic algorithms are a type of evolutionary algorithm that use techniques inspired by Darwinian evolution, such as inheritance, mutation, selection, and crossover. They are commonly used to generate useful solutions to optimization and search problems by evolving candidate solutions over generations. Genetic algorithms work on a population of candidate solutions represented by chromosomes. They evolve toward better solutions through techniques like selection of the fittest solutions, crossover of parent solutions to create new solutions, and random mutation of new solutions. The algorithm terminates when either a maximum number of generations has been produced or a satisfactory fitness level has been reached in the population.
This document discusses classification and decision trees. It defines classification as predicting categorical labels, and lists some common classification methods and examples. It then defines key concepts like attributes, datasets, entropy, information gain, and decision tree components. It provides exercises to calculate entropy and information gain from sample datasets and contingency tables.
This chapter discusses modeling decision processes and decision support systems. It covers the typical modeling process of identifying a problem, analyzing requirements, and identifying variables and relationships. Common error in problem definition is premature focus on solutions rather than fully defining the problem. Tools for structuring problems include influence diagrams and decision trees. Probability can be estimated through frequency, subjectively, or through logic. Techniques for forecasting probabilities include direct estimation, odds forecasting, and comparison forecasting. Sensitivity analysis and value analysis are also discussed.
This document provides an overview of artificial intelligence (AI). It defines AI as the science of developing methods to solve problems usually associated with human intelligence. The document discusses different definitions and visions of AI, including thinking and acting like humans, thinking and acting rationally, and modeling human thinking through computational models. It also covers the history of AI from its origins in the 1940s to recent successes, as well as related fields and main areas of AI research like machine learning, robotics, and natural language processing.
This document summarizes material from a course on advanced topics in artificial intelligence search. It covers uninformed search techniques like depth-first search and breadth-first search as well as informed search techniques like A* search that use heuristics. The document explains that heuristics estimate how close a state is to the goal and discusses properties like admissibility that are required for A* to find optimal solutions. Examples of heuristic functions for pathfinding problems are provided.
The document summarizes key concepts in search-based artificial intelligence including search problems, uninformed search methods, and properties of different search algorithms. It describes search problems as consisting of a state space, successor function, start state, and goal test. It then discusses depth-first search, breadth-first search, and their properties such as completeness, optimality, time complexity, and space complexity.
The document discusses perceptrons and artificial neural networks, including describing the architecture and learning algorithm of a simple perceptron for pattern classification, providing an example of how the perceptron learns from labeled training data to determine the weights and bias that allow it to classify new input patterns, and assigning exercises to modify the perceptron code provided to classify different input data and analyze its convergence over training epochs.
This document discusses backpropagation neural networks. It begins with an introduction to backpropagation and gradient descent optimization. It then describes the architecture of a backpropagation network, including input, hidden, and output layers connected by weights. The training algorithm is explained in detail, including feedforward calculation, backpropagation of error, weight/bias updates, and activation functions. It concludes with discussions of initializing weights randomly or with the Nguyen-Widrow method and a graph showing error reduction over iterations.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
3. Evolutionary
Computation
Evolutionary Computation is the field of study
devoted to the design, development, and analysis is
problem solvers based on natural selection
(simulated evolution).
Evolution has proven to be a powerful search
process.
Evolutionary Computation has been successfully
applied to a wide range of problems including:
Aircraft Design,
Routing in Communications Networks,
Tracking Windshear,
Game Playing (Checkers [Fogel])
4
5. Evolutionary computing algorithms are very common and
used by many researchers in their research to solve the
optimization problems.
Evolutionary Computing algorithms
7. Genetic Algorithms
• A genetic algorithm conceptually follows steps inspired by
the biological processes of evolution.
• GA has been successfully applied to problems that are
difficult to solve using conventional techniques such as
scheduling problems, traveling salesperson problem,
network routing problems and financial marketing.
Genetic Algorithms follow the idea of SURVIVAL OF THE
FITTEST- Better and better solutions evolve from previous
generations until a near optimal solution is obtained.
8. Genetic Algorithms
A genetic algorithm is an iterative procedure that represents
its candidate solutions as strings of genes called
chromosomes.
Genetic Algorithms are often used to improve the
performance of other AI methods such as expert systems or
neural networks.
The method learns by producing offspring that are better
and better as measured by a fitness function, which is a
measure of the objective to be obtained (maximum or
minimum).
9. What is GA
Genetic algorithms are implemented as a computer
simulation in which a population of abstract representations
(called chromosomes or the genotype or the genome) of
candidate solutions (called individuals, creatures, or
phenotypes) to an optimization problem evolves toward
better solutions.
Traditionally, solutions are represented in binary as strings
of 0s and 1s, but other encodings are also possible.
10. Algorithm
BEGIN
Generate initial population;
Compute fitness of each individual;
REPEAT /* New generation /*
FOR population_size / 2 DO
Select two parents from old generation;
/* biased to the fitter ones */
Recombine parents for two offspring;
Compute fitness of offspring;
Insert offspring in new generation
END FOR
UNTIL population has converged
END
11. Genetic learning
algorithm
Step 1: Initialize a population P of n elements as a potential
solution.
Step 2: Until a specified termination condition is satisfied:
2a: Use a fitness function to evaluate each element of
the current solution. If an element passes the
fitness criteria, it remains in P.
2b: The population now contains m elements (m<= n).
Use genetic operators to create (n – m) new
elements. Add the new elements to the population.
13. Digitalized Genetic knowledge representation
A common technique for representing genetic knowledge is
to transform elements into binary strings.
For example, we can represent income range as a string of
two bits for assigning “00” to 20-30k, “01” to 30-40k, and “11”
to 50-60k.
14. Genetic Operator - Crossover
The elements most often used for crossover are those
intended to be eliminated from the population.
Crossover forms new elements for the population by
combining parts of two elements currently in the population.
15. Genetic operator - Mutation
Mutation is carefully applied to elements chosen for
elimination.
Mutation can be applied by randomly flipping bits (or
attribute values) within a single element.
16. Genetic operator - Selection
Selection is to replace to-be-deleted elements by copies of
elements that pass the fitness test with high scores.
With selection, the overall fitness of the population is
guaranteed to increase.
17. Key terms
Individual - Any possible solution
Population - Group of all individuals
Search Space - All possible solutions to the problem
Chromosome - Blueprint for an individual
Locus - The position of a gene on the chromosome
Genome - Collection of all chromosomes for an individual
19. Genetic Algorithm Introduction
Inspired by natural evolution
Population of individuals
Individual is feasible solution to problem
Each individual is characterized by a Fitness function
Higher fitness is better solution
Based on their fitness, parents are selected to reproduce
offspring for a new generation
Fitter individuals have more chance to reproduce
New generation has same size as old generation; old generation dies
Offspring has combination of properties of two parents
If well designed, population will converge to optimal solution
20. 1) Representation
(encoding)
Possible individual’s encoding
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)
21. 2) Initialization
Start with a population of randomly generated
individuals,
or
use A previously saved population
or
A set of solutions provided by a human expert
or
- A set of solutions provided by another heuristic
algorithm
22. 3 ) Selection
Purpose: to focus the search in promising regions of the space
Inspiration: Darwin’s “survival of the fittest”
23. 4 ) Reproduction
Reproduction operators
Crossover
Mutation
Crossover
Two parents produce two offspring
Generally the chance of crossover is between 0.6
and 1.0
Mutation
There is a chance that a gene of a child is changed
randomly
Generally the chance of mutation is low (e.g. 0.001)
24. 4) Reproduction
Operators
1) Crossover
Generating offspring from two selected parents
Single point crossover
Two point crossover (Multi point crossover)
25. One-point crossover 1
Randomly one position in the chromosomes is chosen
Child 1 is head of chromosome of parent 1 with tail of chromosome of parent 2
Child 2 is head of 2 with tail of 1
Randomly chosen position
26. Two-point crossover
Randomly two positions in the chromosomes are chosen
Avoids that genes at the head and genes at the tail of a
chromosome are always split when recombined
28. 5) Evaluation (fitness function)
Solution is only as good as the evaluation function;
choosing a good one is often the hardest part
Similar-encoded solutions should have a similar
fitness
29. 6) Termination condition
A pre-determined number of generations or time has
elapsed
A satisfactory solution has been achieved
No improvement in solution quality has taken place for a
pre-determined number of generations
30. Benefits of GAs
Concept is easy to understand
Supports multi-objective optimization
Always an answer; answer gets better with time
Inherently parallel; easily distributed
31. Example (initialization)
We toss a fair coin 60 times and get the following
initial population:
s1 = 1111010101 f (s1) = 7
s2 = 0111000101 f (s2) = 5
s3 = 1110110101 f (s3) = 7
s4 = 0100010011 f (s4) = 4
s5 = 1110111101 f (s5) = 8
s6 = 0100110000 f (s6) = 3
In first solution with name S1 , first four times head comes so we assign 1111 and
then tail we assign 0,same ten times we make a chromosome of bit of strings
32. Example (selection1)
Next we apply fitness proportionate selection with the
roulette wheel method:
2
1
n
3
Area is
Proportional
to fitness
value
Individual i will have a
probability to be chosen
i
i
f
i
f
)
(
)
(
4
We repeat the extraction
as many times as the
number of individuals we
need to have the same
parent population size
(6 in our case)
33. Example (selection2)
Suppose that, after performing selection, we get the
following population:
s1` = 1111010101 (s1)
s2` = 1110110101 (s3)
s3` = 1110111101 (s5)
s4` = 0111000101 (s2)
s5` = 0100010011 (s4)
s6` = 1110111101 (s5)
34. Example (crossover1)
•Next we mate strings for crossover.
•Suppose that we decide to actually perform crossover
only for couples (s1`, s2`) and (s5`, s6`).
• For each couple, we randomly extract a crossover
point, for instance 2 for the first and 5 for the second
36. Example (mutation1)
The final step is to apply random mutation: for each bit
that we are to copy to the new population we allow a
small probability of error (for instance 0.1)
Before applying mutation:
s1`` = 1110110101
s2`` = 1111010101
s3`` = 1110111101
s4`` = 0111000101
s5`` = 0100011101
s6`` = 1110110011
37. Example (mutation2)
After applying mutation:
s1``` = 1110100101 f (s1``` ) = 6
s2``` = 1111110100f (s2``` ) = 7
s3``` = 1110101111f (s3``` ) = 8
s4``` = 0111000101 f (s4``` ) = 5
s5``` = 0100011101 f (s5``` ) = 5
s6``` = 1110110001 f (s6``` ) = 6
Total number of 1’s after mutations are 37
38. Example
In one generation, the total population fitness
changed from 34 to 37, thus improved by ~9%
At this point, we go through the same process all
over again, until a stopping criterion is met
39. Code
40
# genetic algorithm search of the one max optimization problem
from numpy.random import randint
from numpy.random import rand
# objective function
def onemax(x):
return -sum(x)
# tournament selection
def selection(pop, scores, k=3):
# first random selection
selection_ix = randint(len(pop))
for ix in randint(0, len(pop), k-1):
# check if better (e.g. perform a tournament)
if scores[ix] < scores[selection_ix]:
selection_ix = ix
return pop[selection_ix]
# crossover two parents to create two children
40. def crossover(p1, p2, r_cross):
# children are copies of parents by default
c1, c2 = p1.copy(), p2.copy()
# check for recombination
if rand() < r_cross:
# select crossover point that is not on the end of the string
pt = randint(1, len(p1)-2)
# perform crossover
c1 = p1[:pt] + p2[pt:]
c2 = p2[:pt] + p1[pt:]
return [c1, c2]
# mutation operator
def mutation(bitstring, r_mut):
for i in range(len(bitstring)):
# check for a mutation
if rand() < r_mut:
# flip the bit
bitstring[i] = 1 - bitstring[i]
# genetic algorithm
def genetic_algorithm(objective, n_bits, n_iter, n_pop, r_cross, r_mut):
41
41. # initial population of random bitstring
pop = [randint(0, 2, n_bits).tolist() for _ in range(n_pop)]
# keep track of best solution
best, best_eval = 0, objective(pop[0])
# enumerate generations
for gen in range(n_iter):
# evaluate all candidates in the population
scores = [objective(c) for c in pop]
# check for new best solution
for i in range(n_pop):
if scores[i] < best_eval:
best, best_eval = pop[i], scores[i]
print(">%d, new best f(%s) = %.3f" % (gen,
pop[i], scores[i]))
42
42. # select parents
selected = [selection(pop, scores) for _ in range(n_pop)]
# create the next generation
children = list()
for i in range(0, n_pop, 2):
# get selected parents in pairs
p1, p2 = selected[i], selected[i+1]
# crossover and mutation
for c in crossover(p1, p2, r_cross):
# mutation
mutation(c, r_mut)
# store for next generation
children.append(c)
# replace population
pop = children
return [best, best_eval]
43