Genetic algorithms are a type of evolutionary algorithm used to find optimal or near-optimal solutions to problems by mimicking biological evolution. They work by maintaining a population of candidate solutions and applying genetic operators like selection, crossover and mutation to generate new solutions. A fitness function evaluates the solutions, with more fit solutions being more likely to be selected for reproduction. Over many generations, the population evolves toward an optimal solution. Genetic algorithms are inspired by Darwinian evolution and use techniques like inheritance, mutation, selection and crossover. They have been shown to reliably find good solutions to problems that are not well suited for standard optimization algorithms.
Genetic algorithms are a class of evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover. They work by maintaining a population of potential solutions and applying genetic operators of selection, crossover and mutation to generate new populations in search of an optimal solution. A genetic algorithm begins with a randomly generated population that is evaluated and selected using a fitness function. Selected solutions then reproduce through crossover and mutation to create a new population, and the process repeats until a termination condition is reached.
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 discusses genetic algorithms and their key components. It defines genetic algorithms as search techniques that use principles of evolution and natural genetics to optimize problem solutions. Genetic algorithms work with a population of potential solutions that evolves toward better solutions, selecting individuals with higher fitness and breeding them through crossover and mutation operators to produce new generations. The document outlines the genetic representation of solutions, fitness functions to evaluate solutions, and the basic process of genetic algorithms through generations of selection, crossover and mutation.
Selection of the optimal parameters for machine learning tasks is challenging. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. This presentation gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs. A step-by-step example is given in addition to its implementation in Python 3.5.
---------------------------------
Read more about GA:
Yu, Xinjie, and Mitsuo Gen. Introduction to evolutionary algorithms. Springer Science & Business Media, 2010.
https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html
https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad
This was a presentation for Reading Group 2014 in NUIG. The presentation was based on the research paper: Dai et al. "From Entity Recognition to Entity Linking: a Survey of Advanced Entity Linking Techniques". 2012
This document presents a genetic algorithm approach for learning classification rules from data. The key aspects of the approach are:
1. Binary encoding is used to represent classification rules, with bits indicating attribute values. Rule consequents are determined by the majority class of training examples matched.
2. The fitness function considers error rate, entropy measure, rule consistency, and hole ratio to evaluate rule sets. Error rate measures accuracy, entropy favors homogeneous rule matches, consistency penalizes ambiguous rules, and hole ratio measures coverage.
3. Adaptive asymmetric mutation is applied, with the mutation probability self-adjusting during the algorithm run. Crossover also utilizes two-point crossover of rules.
4. The approach is
This document outlines an approach using latent feature log-linear (LFL) models and ensemble learning to predict student responses on questions for the "What do you know?" challenge on Kaggle. It describes using LFL to model dyadic prediction tasks with side information on students, questions, and student-question pairs. Models are trained on student response data and evaluated on a validation set. Ensemble methods like linear regression and gradient boosted decision trees are used to combine predictions from multiple LFL models to improve performance compared to individual models. The approach achieved strong results on the Kaggle competition.
Travelling salesman problem using genetic algorithms Shivank Shah
This document describes using a genetic algorithm to solve the traveling salesman problem. It defines the traveling salesman problem as finding the shortest route for a salesman to visit each city once and return to their starting city. The method uses a genetic algorithm with operations like generating a random initial population, calculating fitness, selection for crossover using probabilities, crossover using techniques like PMX, and mutation techniques like swapping or flipping parts of routes. The goal is to evolve routes with shorter distances over multiple generations to minimize the total travel distance.
Genetic algorithms are a class of evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover. They work by maintaining a population of potential solutions and applying genetic operators of selection, crossover and mutation to generate new populations in search of an optimal solution. A genetic algorithm begins with a randomly generated population that is evaluated and selected using a fitness function. Selected solutions then reproduce through crossover and mutation to create a new population, and the process repeats until a termination condition is reached.
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 discusses genetic algorithms and their key components. It defines genetic algorithms as search techniques that use principles of evolution and natural genetics to optimize problem solutions. Genetic algorithms work with a population of potential solutions that evolves toward better solutions, selecting individuals with higher fitness and breeding them through crossover and mutation operators to produce new generations. The document outlines the genetic representation of solutions, fitness functions to evaluate solutions, and the basic process of genetic algorithms through generations of selection, crossover and mutation.
Selection of the optimal parameters for machine learning tasks is challenging. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. This presentation gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs. A step-by-step example is given in addition to its implementation in Python 3.5.
---------------------------------
Read more about GA:
Yu, Xinjie, and Mitsuo Gen. Introduction to evolutionary algorithms. Springer Science & Business Media, 2010.
https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html
https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad
This was a presentation for Reading Group 2014 in NUIG. The presentation was based on the research paper: Dai et al. "From Entity Recognition to Entity Linking: a Survey of Advanced Entity Linking Techniques". 2012
This document presents a genetic algorithm approach for learning classification rules from data. The key aspects of the approach are:
1. Binary encoding is used to represent classification rules, with bits indicating attribute values. Rule consequents are determined by the majority class of training examples matched.
2. The fitness function considers error rate, entropy measure, rule consistency, and hole ratio to evaluate rule sets. Error rate measures accuracy, entropy favors homogeneous rule matches, consistency penalizes ambiguous rules, and hole ratio measures coverage.
3. Adaptive asymmetric mutation is applied, with the mutation probability self-adjusting during the algorithm run. Crossover also utilizes two-point crossover of rules.
4. The approach is
This document outlines an approach using latent feature log-linear (LFL) models and ensemble learning to predict student responses on questions for the "What do you know?" challenge on Kaggle. It describes using LFL to model dyadic prediction tasks with side information on students, questions, and student-question pairs. Models are trained on student response data and evaluated on a validation set. Ensemble methods like linear regression and gradient boosted decision trees are used to combine predictions from multiple LFL models to improve performance compared to individual models. The approach achieved strong results on the Kaggle competition.
Travelling salesman problem using genetic algorithms Shivank Shah
This document describes using a genetic algorithm to solve the traveling salesman problem. It defines the traveling salesman problem as finding the shortest route for a salesman to visit each city once and return to their starting city. The method uses a genetic algorithm with operations like generating a random initial population, calculating fitness, selection for crossover using probabilities, crossover using techniques like PMX, and mutation techniques like swapping or flipping parts of routes. The goal is to evolve routes with shorter distances over multiple generations to minimize the total travel distance.
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 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 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. Genetic algorithms are applied to problems with large search spaces or when the solution is unknown.
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.
Genetic algorithms are a class of evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover. They work by generating a population of candidate solutions and evolving them toward increasingly better solutions over multiple generations. Each individual in the population is evaluated to determine its fitness, with more fit solutions being more likely to reproduce and pass on their traits. Offspring are produced through crossover and mutation operations, creating a new population that is used in the next iteration of the algorithm. The process terminates when a satisfactory solution has been found or a maximum number of generations has been produced.
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.
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.
This document discusses artificial neural networks and genetic algorithms. It provides an overview of how artificial neural networks are inspired by biological neural networks and define key components like the input, hidden and output layers. It also explains how genetic algorithms are inspired by natural selection and genetics, using techniques like selection, crossover and mutation to evolve solutions over generations.
This document provides information about genetic algorithms including:
1. Definitions of genetic algorithms from Grefenstette and Goldberg that describe genetic algorithms as search algorithms based on biological evolution and natural selection.
2. An overview of genetic algorithms including the basic concepts of populations, chromosomes, genes, fitness functions, selection, crossover, and mutation.
3. Examples of genetic representations like binary encoding and permutation encoding.
4. Descriptions of genetic operators like selection, crossover, and mutation that maintain genetic diversity between generations.
The document describes using a genetic algorithm to find the maximum values of single-variable functions. It presents the genetic algorithm process, including representation of solutions, initialization, evaluation, selection, and genetic operators. The algorithm is tested on various continuous and non-continuous functions, like polynomials, rationals, trigonometric, and those with asymptotes. The results show that the genetic algorithm can find the true maximum or one very close within a reasonable number of generations, even for complex multi-modal functions that are difficult to optimize with traditional methods.
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
The document describes using a genetic algorithm to find the maximum values of single-variable functions. It discusses:
1) How genetic algorithms work by simulating biological evolution to optimize solutions.
2) Testing the genetic algorithm on continuous and non-continuous functions that are difficult to optimize with traditional methods, such as multimodal, non-differentiable functions.
3) The genetic algorithm was able to find maximum values close to the real maximum for complex test functions, demonstrating its effectiveness at optimizing these difficult single-variable functions.
A Genetic Algorithm Problem Solver For ArchaeologyAmy Cernava
This document introduces a genetic algorithm workbench that can be used to solve archaeology problems. Genetic algorithms are inspired by natural selection and use populations of individuals that evolve over generations to optimize solutions. The workbench allows users to define problems in terms of data types and variables, and evaluate solutions. It implements genetic operators like selection, crossover and mutation to iteratively improve populations. Archaeologists are invited to test genetic algorithms on their problems by using the freely available workbench.
Genetic algorithms are a type of optimization technique inspired by natural selection and genetics. They are commonly used to find optimal or near-optimal solutions to complex problems by generating populations of solutions and using biologically inspired operators like mutation and crossover to select new solutions. Genetic algorithms maintain a population of potential solutions and evolve them over generations to find better solutions to optimization and search problems.
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 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 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. Genetic algorithms are applied to problems with large search spaces or when the solution is unknown.
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.
Genetic algorithms are a class of evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover. They work by generating a population of candidate solutions and evolving them toward increasingly better solutions over multiple generations. Each individual in the population is evaluated to determine its fitness, with more fit solutions being more likely to reproduce and pass on their traits. Offspring are produced through crossover and mutation operations, creating a new population that is used in the next iteration of the algorithm. The process terminates when a satisfactory solution has been found or a maximum number of generations has been produced.
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.
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.
This document discusses artificial neural networks and genetic algorithms. It provides an overview of how artificial neural networks are inspired by biological neural networks and define key components like the input, hidden and output layers. It also explains how genetic algorithms are inspired by natural selection and genetics, using techniques like selection, crossover and mutation to evolve solutions over generations.
This document provides information about genetic algorithms including:
1. Definitions of genetic algorithms from Grefenstette and Goldberg that describe genetic algorithms as search algorithms based on biological evolution and natural selection.
2. An overview of genetic algorithms including the basic concepts of populations, chromosomes, genes, fitness functions, selection, crossover, and mutation.
3. Examples of genetic representations like binary encoding and permutation encoding.
4. Descriptions of genetic operators like selection, crossover, and mutation that maintain genetic diversity between generations.
The document describes using a genetic algorithm to find the maximum values of single-variable functions. It presents the genetic algorithm process, including representation of solutions, initialization, evaluation, selection, and genetic operators. The algorithm is tested on various continuous and non-continuous functions, like polynomials, rationals, trigonometric, and those with asymptotes. The results show that the genetic algorithm can find the true maximum or one very close within a reasonable number of generations, even for complex multi-modal functions that are difficult to optimize with traditional methods.
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
The document describes using a genetic algorithm to find the maximum values of single-variable functions. It discusses:
1) How genetic algorithms work by simulating biological evolution to optimize solutions.
2) Testing the genetic algorithm on continuous and non-continuous functions that are difficult to optimize with traditional methods, such as multimodal, non-differentiable functions.
3) The genetic algorithm was able to find maximum values close to the real maximum for complex test functions, demonstrating its effectiveness at optimizing these difficult single-variable functions.
A Genetic Algorithm Problem Solver For ArchaeologyAmy Cernava
This document introduces a genetic algorithm workbench that can be used to solve archaeology problems. Genetic algorithms are inspired by natural selection and use populations of individuals that evolve over generations to optimize solutions. The workbench allows users to define problems in terms of data types and variables, and evaluate solutions. It implements genetic operators like selection, crossover and mutation to iteratively improve populations. Archaeologists are invited to test genetic algorithms on their problems by using the freely available workbench.
Genetic algorithms are a type of optimization technique inspired by natural selection and genetics. They are commonly used to find optimal or near-optimal solutions to complex problems by generating populations of solutions and using biologically inspired operators like mutation and crossover to select new solutions. Genetic algorithms maintain a population of potential solutions and evolve them over generations to find better solutions to optimization and search problems.
How to Download & Install Module From the Odoo App Store in Odoo 17Celine George
Custom modules offer the flexibility to extend Odoo's capabilities, address unique requirements, and optimize workflows to align seamlessly with your organization's processes. By leveraging custom modules, businesses can unlock greater efficiency, productivity, and innovation, empowering them to stay competitive in today's dynamic market landscape. In this tutorial, we'll guide you step by step on how to easily download and install modules from the Odoo App Store.
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
This presentation was provided by Rebecca Benner, Ph.D., of the American Society of Anesthesiologists, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
CapTechTalks Webinar Slides June 2024 Donovan Wright.pptxCapitolTechU
Slides from a Capitol Technology University webinar held June 20, 2024. The webinar featured Dr. Donovan Wright, presenting on the Department of Defense Digital Transformation.
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
2. IntroductionIntroduction
After scientists became disillusioned withAfter scientists became disillusioned with
classical and neo-classical attempts atclassical and neo-classical attempts at
modeling intelligence, they looked in othermodeling intelligence, they looked in other
directions.directions.
Two prominent fields arose, connectionismTwo prominent fields arose, connectionism
(neural networking, parallel processing) and(neural networking, parallel processing) and
evolutionary computing.evolutionary computing.
It is the latter that this essay deals with -It is the latter that this essay deals with -
genetic algorithms and genetic programming.genetic algorithms and genetic programming.
3. What is GAWhat is GA
AA genetic algorithmgenetic algorithm (or(or GAGA) is a search technique) is a search technique
used in computing to find true or approximateused in computing to find true or approximate
solutions to optimization and search problems.solutions to optimization and search problems.
Genetic algorithms are categorized as global searchGenetic algorithms are categorized as global search
heuristics.heuristics.
Genetic algorithms are a particular class ofGenetic algorithms are a particular class of
evolutionary algorithms that use techniques inspiredevolutionary algorithms that use techniques inspired
by evolutionary biology such as inheritance,by evolutionary biology such as inheritance,
mutation, selection, and crossover (also calledmutation, selection, and crossover (also called
recombination).recombination).
4. What is GAWhat is GA
Genetic algorithms are implemented as a computerGenetic algorithms are implemented as a computer
simulation in which a population of abstractsimulation in which a population of abstract
representations (called chromosomes or the genotyperepresentations (called chromosomes or the genotype
or the genome) of candidate solutions (calledor the genome) of candidate solutions (called
individuals, creatures, or phenotypes) to anindividuals, creatures, or phenotypes) to an
optimization problem evolves toward better solutions.optimization problem evolves toward better solutions.
Traditionally, solutions are represented in binary asTraditionally, solutions are represented in binary as
strings of 0s and 1s, but other encodings are alsostrings of 0s and 1s, but other encodings are also
possible.possible.
5. What is GAWhat is GA
The evolution usually starts from a population ofThe evolution usually starts from a population of
randomly generated individuals and happens inrandomly generated individuals and happens in
generations.generations.
In each generation, the fitness of every individual inIn each generation, the fitness of every individual in
the population is evaluated, multiple individuals arethe population is evaluated, multiple individuals are
selected from the current population (based on theirselected from the current population (based on their
fitness), and modified (recombined and possiblyfitness), and modified (recombined and possibly
mutated) to form a new population.mutated) to form a new population.
6. What is GAWhat is GA
The new population is then used in the nextThe new population is then used in the next
iteration of the algorithm.iteration of the algorithm.
Commonly, the algorithm terminates whenCommonly, the algorithm terminates when
either a maximum number of generations haseither a maximum number of generations has
been produced, or a satisfactory fitness levelbeen produced, or a satisfactory fitness level
has been reached for the population.has been reached for the population.
If the algorithm has terminated due to aIf the algorithm has terminated due to a
maximum number of generations, amaximum number of generations, a
satisfactory solution may or may not havesatisfactory solution may or may not have
been reached.been reached.
7. Key termsKey terms
IndividualIndividual - Any possible solution- Any possible solution
PopulationPopulation - Group of all- Group of all individualsindividuals
Search SpaceSearch Space - All possible solutions to the problem- All possible solutions to the problem
ChromosomeChromosome - Blueprint for an- Blueprint for an individualindividual
TraitTrait - Possible aspect (- Possible aspect (features)features) of anof an individualindividual
Allele -- Possible settings of trait (black, blond, etc.)
LocusLocus - The position of a- The position of a genegene on theon the chromosomechromosome
GenomeGenome - Collection of all- Collection of all chromosomeschromosomes for anfor an
individualindividual
9. Genotype and Phenotype
Genotype:
– Particular set of genes in a genome
Phenotype:
– Physical characteristic of the genotype
(smart, beautiful, healthy, etc.)
11. GA RequirementsGA Requirements
A typical genetic algorithm requires two things to be defined:A typical genetic algorithm requires two things to be defined:
a genetic representation of the solution domain, anda genetic representation of the solution domain, and
a fitness function to evaluate the solution domain.a fitness function to evaluate the solution domain.
A standard representation of the solution is as an array of bits.A standard representation of the solution is as an array of bits.
Arrays of other types and structures can be used in essentiallyArrays of other types and structures can be used in essentially
the same way.the same way.
The main property that makes these genetic representationsThe main property that makes these genetic representations
convenient is that their parts are easily aligned due to theirconvenient is that their parts are easily aligned due to their
fixed size, that facilitates simple crossover operation.fixed size, that facilitates simple crossover operation.
Variable length representations may also be used, butVariable length representations may also be used, but
crossover implementation is more complex in this case.crossover implementation is more complex in this case.
Tree-like representations are explored in GeneticTree-like representations are explored in Genetic
programming.programming.
12. RepresentationRepresentation
Chromosomes could be:Chromosomes could be:
Bit strings (0101 ... 1100)Bit strings (0101 ... 1100)
Real numbers (43.2 -33.1 ... 0.0 89.2)Real numbers (43.2 -33.1 ... 0.0 89.2)
Permutations of element (E11 E3 E7 ... E1 E15)Permutations of element (E11 E3 E7 ... E1 E15)
Lists of rules (R1 R2 R3 ... R22 R23)Lists of rules (R1 R2 R3 ... R22 R23)
Program elements (genetic programming)Program elements (genetic programming)
... any data structure ...... any data structure ...
13. GA RequirementsGA Requirements
The fitness function is defined over the genetic representationThe fitness function is defined over the genetic representation
and measures theand measures the qualityquality of the represented solution.of the represented solution.
The fitness function is always problem dependent.The fitness function is always problem dependent.
For instance, in theFor instance, in the knapsack problemknapsack problem we want to maximizewe want to maximize
the total value of objects that we can put in a knapsack ofthe total value of objects that we can put in a knapsack of
some fixed capacity.some fixed capacity.
A representation of a solution might be an array of bits, whereA representation of a solution might be an array of bits, where
each bit represents a different object, and the value of the biteach bit represents a different object, and the value of the bit
(0 or 1) represents whether or not the object is in the knapsack.(0 or 1) represents whether or not the object is in the knapsack.
Not every such representation is valid, as the size of objectsNot every such representation is valid, as the size of objects
may exceed the capacity of the knapsack.may exceed the capacity of the knapsack.
TheThe fitnessfitness of the solution is the sum of values of all objects inof the solution is the sum of values of all objects in
the knapsack if the representation is valid, or 0 otherwise. Inthe knapsack if the representation is valid, or 0 otherwise. In
some problems, it is hard or even impossible to define thesome problems, it is hard or even impossible to define the
fitness expression; in these cases, interactive geneticfitness expression; in these cases, interactive genetic
algorithms are used.algorithms are used.
15. Basics of GABasics of GA
The most common type of genetic algorithm works like this:The most common type of genetic algorithm works like this:
a population is created with a group of individuals createda population is created with a group of individuals created
randomly.randomly.
The individuals in the population are then evaluated.The individuals in the population are then evaluated.
The evaluation function is provided by the programmer andThe evaluation function is provided by the programmer and
gives the individuals a score based on how well they performgives the individuals a score based on how well they perform
at the given task.at the given task.
Two individuals are then selected based on their fitness, theTwo individuals are then selected based on their fitness, the
higher the fitness, the higher the chance of being selected.higher the fitness, the higher the chance of being selected.
These individuals then "reproduce" to create one or moreThese individuals then "reproduce" to create one or more
offspring, after which the offspring are mutated randomly.offspring, after which the offspring are mutated randomly.
This continues until a suitable solution has been found or aThis continues until a suitable solution has been found or a
certain number of generations have passed, depending on thecertain number of generations have passed, depending on the
needs of the programmer.needs of the programmer.
16. General Algorithm for GAGeneral Algorithm for GA
InitializationInitialization
Initially many individual solutions are randomlyInitially many individual solutions are randomly
generated to form an initial population. Thegenerated to form an initial population. The
population size depends on the nature of the problem,population size depends on the nature of the problem,
but typically contains several hundreds or thousandsbut typically contains several hundreds or thousands
of possible solutions.of possible solutions.
Traditionally, the population is generated randomly,Traditionally, the population is generated randomly,
covering the entire range of possible solutions (thecovering the entire range of possible solutions (the
search spacesearch space).).
Occasionally, the solutions may be "seeded" in areasOccasionally, the solutions may be "seeded" in areas
where optimal solutions are likely to be found.where optimal solutions are likely to be found.
17. General Algorithm for GAGeneral Algorithm for GA
SelectionSelection
During each successive generation, a proportion of theDuring each successive generation, a proportion of the
existing population is selected to breed a new generation.existing population is selected to breed a new generation.
Individual solutions are selected through aIndividual solutions are selected through a fitness-basedfitness-based
process, where fitter solutions (as measured by a fitnessprocess, where fitter solutions (as measured by a fitness
function) are typically more likely to be selected.function) are typically more likely to be selected.
Certain selection methods rate the fitness of each solution andCertain selection methods rate the fitness of each solution and
preferentially select the best solutions. Other methods ratepreferentially select the best solutions. Other methods rate
only a random sample of the population, as this process mayonly a random sample of the population, as this process may
be very time-consuming.be very time-consuming.
Most functions are stochastic and designed so that a smallMost functions are stochastic and designed so that a small
proportion of less fit solutions are selected. This helps keepproportion of less fit solutions are selected. This helps keep
the diversity of the population large, preventing prematurethe diversity of the population large, preventing premature
convergence on poor solutions. Popular and well-studiedconvergence on poor solutions. Popular and well-studied
selection methods include roulette wheel selection andselection methods include roulette wheel selection and
tournament selection.tournament selection.
18. General Algorithm for GAGeneral Algorithm for GA
In roulette wheel selection, individuals areIn roulette wheel selection, individuals are
given a probability of being selected that isgiven a probability of being selected that is
directly proportionate to their fitness.directly proportionate to their fitness.
Two individuals are then chosen randomlyTwo individuals are then chosen randomly
based on these probabilities and producebased on these probabilities and produce
offspring.offspring.
19. General Algorithm for GAGeneral Algorithm for GA
Roulette Wheel’s Selection Pseudo Code:Roulette Wheel’s Selection Pseudo Code:
for all members of populationfor all members of population
sum += fitness of this individualsum += fitness of this individual
end forend for
for all members of populationfor all members of population
probability = sum of probabilities + (fitness / sum)probability = sum of probabilities + (fitness / sum)
sum of probabilities += probabilitysum of probabilities += probability
end forend for
loop until new population is fullloop until new population is full
do this twicedo this twice
number = Random between 0 and 1number = Random between 0 and 1
for all members of populationfor all members of population
if number > probability but less than next probability thenif number > probability but less than next probability then
you have been selectedyou have been selected
end forend for
endend
create offspringcreate offspring
end loopend loop
20. General Algorithm for GAGeneral Algorithm for GA
ReproductionReproduction
The next step is to generate a second generation population ofThe next step is to generate a second generation population of
solutions from those selected through genetic operators:solutions from those selected through genetic operators:
crossover (also called recombination), and/or mutation.crossover (also called recombination), and/or mutation.
For each new solution to be produced, a pair of "parent"For each new solution to be produced, a pair of "parent"
solutions is selected for breeding from the pool selectedsolutions is selected for breeding from the pool selected
previously.previously.
By producing a "child" solution using the above methods ofBy producing a "child" solution using the above methods of
crossover and mutation, a new solution is created whichcrossover and mutation, a new solution is created which
typically shares many of the characteristics of its "parents".typically shares many of the characteristics of its "parents".
New parents are selected for each child, and the processNew parents are selected for each child, and the process
continues until a new population of solutions of appropriatecontinues until a new population of solutions of appropriate
size is generated.size is generated.
21. General Algorithm for GAGeneral Algorithm for GA
These processes ultimately result in the nextThese processes ultimately result in the next
generation population of chromosomes that isgeneration population of chromosomes that is
different from the initial generation.different from the initial generation.
Generally the average fitness will haveGenerally the average fitness will have
increased by this procedure for the population,increased by this procedure for the population,
since only the best organisms from the firstsince only the best organisms from the first
generation are selected for breeding, alonggeneration are selected for breeding, along
with a small proportion of less fit solutions, forwith a small proportion of less fit solutions, for
reasons already mentioned above.reasons already mentioned above.
22. CrossoverCrossover
the most common type is single point crossover. In singlethe most common type is single point crossover. In single
point crossover, you choose a locus at which you swap thepoint crossover, you choose a locus at which you swap the
remaining alleles from on parent to the other. This is complexremaining alleles from on parent to the other. This is complex
and is best understood visually.and is best understood visually.
As you can see, the children take one section of theAs you can see, the children take one section of the
chromosome from each parent.chromosome from each parent.
The point at which the chromosome is broken depends on theThe point at which the chromosome is broken depends on the
randomly selected crossover point.randomly selected crossover point.
This particular method is called single point crossover becauseThis particular method is called single point crossover because
only one crossover point exists. Sometimes only child 1 oronly one crossover point exists. Sometimes only child 1 or
child 2 is created, but oftentimes both offspring are createdchild 2 is created, but oftentimes both offspring are created
and put into the new population.and put into the new population.
Crossover does not always occur, however. Sometimes, basedCrossover does not always occur, however. Sometimes, based
on a set probability, no crossover occurs and the parents areon a set probability, no crossover occurs and the parents are
copied directly to the new population. The probability ofcopied directly to the new population. The probability of
crossover occurring is usually 60% to 70%.crossover occurring is usually 60% to 70%.
24. MutationMutation
After selection and crossover, you now have a new populationAfter selection and crossover, you now have a new population
full of individuals.full of individuals.
Some are directly copied, and others are produced bySome are directly copied, and others are produced by
crossover.crossover.
In order to ensure that the individuals are not all exactly theIn order to ensure that the individuals are not all exactly the
same, you allow for a small chance of mutation.same, you allow for a small chance of mutation.
You loop through all the alleles of all the individuals, and ifYou loop through all the alleles of all the individuals, and if
that allele is selected for mutation, you can either change it bythat allele is selected for mutation, you can either change it by
a small amount or replace it with a new value. The probabilitya small amount or replace it with a new value. The probability
of mutation is usually between 1 and 2 tenths of a percent.of mutation is usually between 1 and 2 tenths of a percent.
Mutation is fairly simple. You just change the selected allelesMutation is fairly simple. You just change the selected alleles
based on what you feel is necessary and move on. Mutation is,based on what you feel is necessary and move on. Mutation is,
however, vital to ensuring genetic diversity within thehowever, vital to ensuring genetic diversity within the
population.population.
26. General Algorithm for GAGeneral Algorithm for GA
TerminationTermination
This generational process is repeated until aThis generational process is repeated until a
termination condition has been reached.termination condition has been reached.
Common terminating conditions are:Common terminating conditions are:
A solution is found that satisfies minimum criteriaA solution is found that satisfies minimum criteria
Fixed number of generations reachedFixed number of generations reached
Allocated budget (computation time/money) reachedAllocated budget (computation time/money) reached
The highest ranking solution's fitness is reaching or hasThe highest ranking solution's fitness is reaching or has
reached a plateau such that successive iterations no longerreached a plateau such that successive iterations no longer
produce better resultsproduce better results
Manual inspectionManual inspection
Any Combinations of the aboveAny Combinations of the above
30. Genetic ProgrammingGenetic Programming
In programming languages such as LISP, the mathematicalIn programming languages such as LISP, the mathematical
notation is not written in standard notation, but in prefixnotation is not written in standard notation, but in prefix
notation. Some examples of this:notation. Some examples of this:
+ 2 1+ 2 1 :: 2 + 12 + 1
* + 2 1 2* + 2 1 2 :: 2 * (2+1)2 * (2+1)
* + - 2 1 4 9 :* + - 2 1 4 9 : 9 * ((2 - 1) + 4)9 * ((2 - 1) + 4)
Notice the difference between the left-hand side to the right?Notice the difference between the left-hand side to the right?
Apart from the order being different, no parenthesis! TheApart from the order being different, no parenthesis! The
prefix method makes it a lot easier for programmers andprefix method makes it a lot easier for programmers and
compilers alike, because order precedence is not an issue.compilers alike, because order precedence is not an issue.
You can build expression trees out of these strings that thenYou can build expression trees out of these strings that then
can be easily evaluated, for example, here are the trees for thecan be easily evaluated, for example, here are the trees for the
above three expressions.above three expressions.
32. Genetic ProgrammingGenetic Programming
You can see how expression evaluation is thus a lotYou can see how expression evaluation is thus a lot
easier.easier.
What this have to do with GAs? If for example youWhat this have to do with GAs? If for example you
have numerical data and 'answers', but no expressionhave numerical data and 'answers', but no expression
to conjoin the data with the answers.to conjoin the data with the answers.
A genetic algorithm can be used to 'evolve' anA genetic algorithm can be used to 'evolve' an
expression tree to create a very close fit to the data.expression tree to create a very close fit to the data.
By 'splicing' and 'grafting' the trees and evaluatingBy 'splicing' and 'grafting' the trees and evaluating
the resulting expression with the data and testing it tothe resulting expression with the data and testing it to
the answers, the fitness function can return how closethe answers, the fitness function can return how close
the expression is.the expression is.
33. Applications of Genetic AlgorithmsApplications of Genetic Algorithms
Uses of genetic programming can lie in:Uses of genetic programming can lie in:
stock market predictionstock market prediction
advanced mathematicsadvanced mathematics
and military applicationsand military applications
Composing MusicComposing Music
GamingGaming
Market StrategiesMarket Strategies
RoboticsRobotics