This document provides an introduction to evolutionary computing. It begins with an overview of the basic EC metaphor which is inspired by Darwinian evolution and natural genetics. It then discusses the history of EC, from early proposals in the 1940s-1960s to its rise in popularity starting in the 1980s. The document also reviews key concepts from Darwinian evolution like survival of the fittest, genetic variation and recombination, and adaptive landscapes. It describes how EC can be applied to problems like optimization, modeling and simulation. Overall, the document outlines the biological foundations and increasing applications of evolutionary computation.
Positioning of EC and the basic EC metaphor
Historical perspective
Biological inspiration:
Darwinian evolution theory (simplified!)
Genetics (simplified!)
Motivation for EC
What can EC do: examples of application areas
Demo: evolutionary magic square solver
These slides contain Brief knowledge of genetic algorithms.
Which are enough of you B.tech CSE semester exams . Selection, Crossover, Mutation concepts discussed with math and explanation
Genetic algorithms were developed in the 1970s in the USA by researchers like John Holland. They are typically applied to discrete optimization problems and use techniques inspired by genetics, like crossover and mutation, to evolve solutions over multiple generations. The original genetic algorithm, called the simple genetic algorithm, uses binary representations of solutions and operates by selecting parents based on fitness, applying crossover and mutation to produce offspring, and replacing the whole population with the new offspring. There have since been many variants developed that use different representations, operators, and selection methods.
Genetic algorithms are a type of evolutionary algorithm developed in the 1970s. They are inspired by Darwinian evolution and use techniques like mutation, crossover and selection. The original genetic algorithm, called the simple genetic algorithm, represents solutions as binary strings and uses one-point crossover and bit-flip mutation. It has been improved upon with different representations, operators, and selection mechanisms, but provides a useful benchmark for testing new genetic algorithms.
Genetic algorithms are a type of evolutionary algorithm developed in the 1970s. They are typically applied to discrete optimization problems and use techniques inspired by natural selection, such as crossover and mutation to evolve solutions. John Holland developed the original simple genetic algorithm which uses binary representations of solutions and operates by selecting parents based on fitness, crossing over and mutating their bits to produce offspring for the next generation. There have since been many variants developed that use different representations, operators, and selection methods.
The document discusses genetic algorithms and their components. It describes how genetic algorithms were developed in the 1970s and are typically applied to discrete optimization problems. The simple genetic algorithm uses binary representations, crossover and mutation operators, and fitness-proportionate selection. Variations involve different representations, operators, and selection mechanisms. The document provides examples and details of genetic algorithm components like mutation, crossover, selection, and their application to different problem representations.
Biological psychology is the study of the physiological, evolutionary, and developmental mechanisms of behavior and experience, with an emphasis on studying brain areas and how neuron activity produces behavior. There are four main biological explanations of behavior: physiological relates behavior to brain activity; ontogenetic examines development; evolutionary looks at evolutionary history; and functional describes why a structure or behavior evolved. The mind-body problem debates whether the mind and body are separate or the same, and questions the nature of consciousness. Genetics research examines inheritance of traits through genes and DNA, and the influences of environment. Evolution occurs through genetic changes over generations, and sociobiology/evolutionary psychology seeks functional explanations for behaviors. Animal research is used to study behavior but raises ethical
Genetic algorithms are a class of evolutionary algorithms inspired by Darwinian evolution. They were developed in the 1970s and are typically applied to discrete optimization problems. The simple genetic algorithm uses binary representations, one-point or uniform crossover, bit-flip mutation, and fitness-proportionate selection. It emphasizes combining information from parents through crossover. While effective, it has some limitations like restrictive representations and sensitivity to converging populations. Many variants of genetic algorithms have been developed that use different representations, operators, and mechanisms to address these issues.
Positioning of EC and the basic EC metaphor
Historical perspective
Biological inspiration:
Darwinian evolution theory (simplified!)
Genetics (simplified!)
Motivation for EC
What can EC do: examples of application areas
Demo: evolutionary magic square solver
These slides contain Brief knowledge of genetic algorithms.
Which are enough of you B.tech CSE semester exams . Selection, Crossover, Mutation concepts discussed with math and explanation
Genetic algorithms were developed in the 1970s in the USA by researchers like John Holland. They are typically applied to discrete optimization problems and use techniques inspired by genetics, like crossover and mutation, to evolve solutions over multiple generations. The original genetic algorithm, called the simple genetic algorithm, uses binary representations of solutions and operates by selecting parents based on fitness, applying crossover and mutation to produce offspring, and replacing the whole population with the new offspring. There have since been many variants developed that use different representations, operators, and selection methods.
Genetic algorithms are a type of evolutionary algorithm developed in the 1970s. They are inspired by Darwinian evolution and use techniques like mutation, crossover and selection. The original genetic algorithm, called the simple genetic algorithm, represents solutions as binary strings and uses one-point crossover and bit-flip mutation. It has been improved upon with different representations, operators, and selection mechanisms, but provides a useful benchmark for testing new genetic algorithms.
Genetic algorithms are a type of evolutionary algorithm developed in the 1970s. They are typically applied to discrete optimization problems and use techniques inspired by natural selection, such as crossover and mutation to evolve solutions. John Holland developed the original simple genetic algorithm which uses binary representations of solutions and operates by selecting parents based on fitness, crossing over and mutating their bits to produce offspring for the next generation. There have since been many variants developed that use different representations, operators, and selection methods.
The document discusses genetic algorithms and their components. It describes how genetic algorithms were developed in the 1970s and are typically applied to discrete optimization problems. The simple genetic algorithm uses binary representations, crossover and mutation operators, and fitness-proportionate selection. Variations involve different representations, operators, and selection mechanisms. The document provides examples and details of genetic algorithm components like mutation, crossover, selection, and their application to different problem representations.
Biological psychology is the study of the physiological, evolutionary, and developmental mechanisms of behavior and experience, with an emphasis on studying brain areas and how neuron activity produces behavior. There are four main biological explanations of behavior: physiological relates behavior to brain activity; ontogenetic examines development; evolutionary looks at evolutionary history; and functional describes why a structure or behavior evolved. The mind-body problem debates whether the mind and body are separate or the same, and questions the nature of consciousness. Genetics research examines inheritance of traits through genes and DNA, and the influences of environment. Evolution occurs through genetic changes over generations, and sociobiology/evolutionary psychology seeks functional explanations for behaviors. Animal research is used to study behavior but raises ethical
Genetic algorithms are a class of evolutionary algorithms inspired by Darwinian evolution. They were developed in the 1970s and are typically applied to discrete optimization problems. The simple genetic algorithm uses binary representations, one-point or uniform crossover, bit-flip mutation, and fitness-proportionate selection. It emphasizes combining information from parents through crossover. While effective, it has some limitations like restrictive representations and sensitivity to converging populations. Many variants of genetic algorithms have been developed that use different representations, operators, and mechanisms to address these issues.
The document discusses key concepts in genetics and evolutionary theory. It explains that DNA stores and transmits genetic information from generation to generation. Genes are segments of DNA that code for proteins. There are two main sources of genetic variation: mutation, which causes changes in DNA sequences, and gene shuffling during sexual reproduction, which creates new combinations of genes. Genetic variation provides the raw material for evolution and allows species to evolve to keep up with their environment or face extinction.
This document summarizes genetics, DNA, heredity, and related concepts. It explains that genes located on chromosomes contain DNA, which codes for traits that are passed from parents to offspring. DNA is made up of four nitrogenous bases that form the ladder-like structure of the DNA double helix. DNA replicates before cell division to make copies for new cells. Mutations can occur in DNA and affect traits in offspring. Gene expression determines which genes are activated in different cell types.
This document provides an overview of a General Biology 2 course. It includes 3 modules that cover genetics, evolution and origin of biodiversity, and systematics based on evolutionary relationships. The genetics module provides an introduction to DNA replication and Gregor Mendel's discoveries in genetics. It defines key terms in Mendelian genetics like alleles, carriers, cross-pollination, dominant alleles, and genotypes. The module also lists contrasting characters that Mendel studied in garden peas.
This document provides an introduction to genetics. It discusses the history of genetics, including early theories on heredity from Greek philosophers and biologists from the 15th-19th centuries. It also covers modern concepts including Mendel's experiments, transmission genetics, molecular and biochemical genetics, and population and biometrical genetics. The document concludes by outlining several applications of genetics in areas like agriculture, industry, health/medicine, environment, and forensics.
This document summarizes genetics, DNA, heredity, and gene expression. It explains that genes located on chromosomes contain DNA, which codes for traits that are passed from parents to offspring. DNA is made up of four nitrogenous bases that form a double helix structure. DNA replicates before cell division by unwinding and using each strand as a template to make a new complementary strand. Gene expression and mutations can influence the traits that are expressed in offspring.
First year SBC174 Evolution course - week 2
1. NeoDarwinism/ModernSynthesis
2. Major transitions in Evolution
3. Geological Timescales
4. Some drivers of evolution
Evolutionary Genetics by: Kim Jim F. Raborar, RN, MAEd(ue)Kim Jim Raborar
This presentation was created as a partial fulfillment of the requirements in the subject Advanced Genetics. Everything that was here were kinda symbolic. I mean, you could recognize that this was a product of so much data interpretation. I therefore suggest you read and read a lot first before you go back to this presentation. Or you could just contact me so i could send you the key-pointers.
Have a super nice day.
Kimy
This document provides an overview of biology concepts including:
1) Eukaryotic cells have a nucleus while prokaryotic cells do not. Both can undergo cellular respiration to produce ATP.
2) The plasma membrane controls what enters and exits the cell and is important for homeostasis. Enzymes speed up cellular reactions.
3) DNA is replicated before cell division and contains instructions for making proteins via transcription and translation.
4) Evolution occurs over time through natural selection, where organisms with favorable variations are more likely to survive and reproduce.
This document provides an overview of key concepts from the Biology 212 course on Biochemistry, The Cell, and Genetics. It discusses the five unifying themes of biology, including heritable information, organization and emergent properties, interactions, energy and matter transfer, and evolution. It also covers topics like cellular organization, DNA and inheritance, systems biology approaches, and examples of scientific inquiry. The document uses diagrams and examples to illustrate these fundamental biological principles at various levels of organization from molecules to ecosystems.
Bio chapter 1 biochemistry, the cell, & geneticsAngel Vega
Evolution, the Themes of Biology, and Scientific Inquiry

KEY CONCEPTS
1.1 The study of life reveals common themes
1.2 The Core Theme: Evolution accounts for the unity and
diversity of life
1.3 In studying nature, scientists make observations and form and test hypotheses
1.4 Science benefits from a cooperative approach and
diverse viewpoints
This document provides an overview of key concepts from the Biology 212 course on Biochemistry, The Cell, and Genetics. It discusses the five unifying themes of biology, which are that all living things share heritable genetic information, organization and emergent properties, interactions with the environment, use of energy and matter, and evolution. Examples are given to illustrate these themes at different levels of biological organization from molecules to ecosystems. Key topics covered include cells, DNA, heredity, organization, biochemistry, and natural selection.
This document provides an overview of epigenetics concepts and environmental influences on epigenetics. It begins with introductory definitions of epigenetics and mechanisms like DNA methylation and histone modifications. It then discusses how the environment can influence the epigenome and provides examples like how air pollution exposure is associated with changes in DNA methylation and blood pressure. It also discusses how parental experiences like odor exposure can influence offspring behavior and neural structure through epigenetic changes in sperm. The document concludes by discussing epigenome-wide study approaches to investigate associations between exposures, epigenetic marks, and disease across the genome.
The document discusses concepts related to ontogeny (development of an organism) and phylogeny (evolutionary history). It describes:
1) Epigenesis, the unfolding development of an organism from an egg or spore through differentiation of cells and formation of organs, in contrast to preformationist theories.
2) How altered ontogeny, such as changes in timing of developmental events (heterochrony), can produce phylogenetic changes through processes like neoteny or acceleration.
3) De Beer's eight ways that changes in ontogeny can lead to phylogenetic changes, simplified by Gould into changes in relative timing of developmental events.
This lecture provides an overview of DNA and uses HIV as a case study to demonstrate evolutionary concepts. It reviews that DNA is made up of nucleotides that pair in specific ways, and is located in the nucleus as well as mitochondria and chloroplasts. HIV exemplifies evolution through mutation and selection, as early drug treatments led to the rise of pre-existing resistant viral mutants. Phylogenetic analysis of HIV strains helps show transmission relationships and the historical origins of the virus in different primate species.
Introduction of Animal Genetics & History of GeneticsAashish Patel
This document provides an overview of genetics including key discoveries and scientists. It discusses Gregor Mendel's foundational work in 1866 and subsequent rediscovery of his principles. Important milestones are highlighted such as Watson and Crick's discovery of DNA structure in 1953. The document also covers branches of genetics, pre-Mendelian concepts of heredity, and applications of genetics in fields like taxonomy, veterinary medicine, and evolution.
This document provides an overview of key themes in biology, including:
1) Biology can be studied at different levels from molecules to ecosystems, and new properties emerge at each level.
2) Organisms interact with their environments, exchanging matter and energy.
3) Evolution accounts for the unity and diversity of life through common descent and natural selection.
Plants reproduce through both sexual and asexual reproduction. Sexual reproduction involves the fusion of male and female gametes to form seeds and fruits. Asexual reproduction occurs through vegetative structures like roots, stems, and leaves developing into new plants via methods such as cuttings, layering, and grafting. Genes contain DNA and are located on chromosomes, with humans having 23 chromosome pairs. DNA stores and transmits genetic information through the bases adenine, guanine, cytosine, and thymine bonding together to form structures like genes and chromosomes. Mutations can be inherited or acquired, and genetic engineering allows transferring genes between different species.
Plants can reproduce sexually through the fusion of male and female gametes to form seeds and fruits, or asexually through vegetative reproduction methods like cuttings, layering, grafting, and budding. Sexual reproduction mainly occurs in flowering plants. DNA contains genetic information stored as base pairs of adenine, guanine, cytosine, and thymine, and is packaged into chromosomes within the nucleus of cells. Genes transfer this genetic information through processes like replication, transcription, and translation. Mutations can occur through changes in DNA sequence, and genetic engineering allows transferring genes between different species.
This document provides an overview of a guest lecture on molecular genetics and population genetics. The lecture aims to review Mendelian genetics and its relationship to population genetics. It will introduce the field of population genetics and its significance. The lecturer, Hasan Alhaddad, will provide background on his education and research experience. He will review theories of inheritance pre-Mendel, Mendel's experiments with pea plants, evolution by natural selection, and how population genetics integrates genetics and evolution.
The document provides a timeline of the evolution of evolutionary thought from ancient Greek philosophers like Plato and Aristotle, through the 17th-18th centuries when naturalists began exploring new lands and observing similarities between species. It then covers the early 19th century work of Lamarck on inheritance of acquired traits and Darwin and Wallace's independent development of the theory of evolution by natural selection. The theory gained acceptance after Mendel's work on genetics supported Darwin's ideas about inheritance of traits. Later sections discuss evidence of evolution from fossils, comparative anatomy and embryology, biochemistry and molecular biology, and population genetics.
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.
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.pptHenry Hollis
The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
Confiscations, Kotahitanga, Kingitanga, Parliament, Suffrage, Repudiation, Economic Change, Agriculture, Gold Mining, Timber, Flax, Sheep, Dairying,
The document discusses key concepts in genetics and evolutionary theory. It explains that DNA stores and transmits genetic information from generation to generation. Genes are segments of DNA that code for proteins. There are two main sources of genetic variation: mutation, which causes changes in DNA sequences, and gene shuffling during sexual reproduction, which creates new combinations of genes. Genetic variation provides the raw material for evolution and allows species to evolve to keep up with their environment or face extinction.
This document summarizes genetics, DNA, heredity, and related concepts. It explains that genes located on chromosomes contain DNA, which codes for traits that are passed from parents to offspring. DNA is made up of four nitrogenous bases that form the ladder-like structure of the DNA double helix. DNA replicates before cell division to make copies for new cells. Mutations can occur in DNA and affect traits in offspring. Gene expression determines which genes are activated in different cell types.
This document provides an overview of a General Biology 2 course. It includes 3 modules that cover genetics, evolution and origin of biodiversity, and systematics based on evolutionary relationships. The genetics module provides an introduction to DNA replication and Gregor Mendel's discoveries in genetics. It defines key terms in Mendelian genetics like alleles, carriers, cross-pollination, dominant alleles, and genotypes. The module also lists contrasting characters that Mendel studied in garden peas.
This document provides an introduction to genetics. It discusses the history of genetics, including early theories on heredity from Greek philosophers and biologists from the 15th-19th centuries. It also covers modern concepts including Mendel's experiments, transmission genetics, molecular and biochemical genetics, and population and biometrical genetics. The document concludes by outlining several applications of genetics in areas like agriculture, industry, health/medicine, environment, and forensics.
This document summarizes genetics, DNA, heredity, and gene expression. It explains that genes located on chromosomes contain DNA, which codes for traits that are passed from parents to offspring. DNA is made up of four nitrogenous bases that form a double helix structure. DNA replicates before cell division by unwinding and using each strand as a template to make a new complementary strand. Gene expression and mutations can influence the traits that are expressed in offspring.
First year SBC174 Evolution course - week 2
1. NeoDarwinism/ModernSynthesis
2. Major transitions in Evolution
3. Geological Timescales
4. Some drivers of evolution
Evolutionary Genetics by: Kim Jim F. Raborar, RN, MAEd(ue)Kim Jim Raborar
This presentation was created as a partial fulfillment of the requirements in the subject Advanced Genetics. Everything that was here were kinda symbolic. I mean, you could recognize that this was a product of so much data interpretation. I therefore suggest you read and read a lot first before you go back to this presentation. Or you could just contact me so i could send you the key-pointers.
Have a super nice day.
Kimy
This document provides an overview of biology concepts including:
1) Eukaryotic cells have a nucleus while prokaryotic cells do not. Both can undergo cellular respiration to produce ATP.
2) The plasma membrane controls what enters and exits the cell and is important for homeostasis. Enzymes speed up cellular reactions.
3) DNA is replicated before cell division and contains instructions for making proteins via transcription and translation.
4) Evolution occurs over time through natural selection, where organisms with favorable variations are more likely to survive and reproduce.
This document provides an overview of key concepts from the Biology 212 course on Biochemistry, The Cell, and Genetics. It discusses the five unifying themes of biology, including heritable information, organization and emergent properties, interactions, energy and matter transfer, and evolution. It also covers topics like cellular organization, DNA and inheritance, systems biology approaches, and examples of scientific inquiry. The document uses diagrams and examples to illustrate these fundamental biological principles at various levels of organization from molecules to ecosystems.
Bio chapter 1 biochemistry, the cell, & geneticsAngel Vega
Evolution, the Themes of Biology, and Scientific Inquiry

KEY CONCEPTS
1.1 The study of life reveals common themes
1.2 The Core Theme: Evolution accounts for the unity and
diversity of life
1.3 In studying nature, scientists make observations and form and test hypotheses
1.4 Science benefits from a cooperative approach and
diverse viewpoints
This document provides an overview of key concepts from the Biology 212 course on Biochemistry, The Cell, and Genetics. It discusses the five unifying themes of biology, which are that all living things share heritable genetic information, organization and emergent properties, interactions with the environment, use of energy and matter, and evolution. Examples are given to illustrate these themes at different levels of biological organization from molecules to ecosystems. Key topics covered include cells, DNA, heredity, organization, biochemistry, and natural selection.
This document provides an overview of epigenetics concepts and environmental influences on epigenetics. It begins with introductory definitions of epigenetics and mechanisms like DNA methylation and histone modifications. It then discusses how the environment can influence the epigenome and provides examples like how air pollution exposure is associated with changes in DNA methylation and blood pressure. It also discusses how parental experiences like odor exposure can influence offspring behavior and neural structure through epigenetic changes in sperm. The document concludes by discussing epigenome-wide study approaches to investigate associations between exposures, epigenetic marks, and disease across the genome.
The document discusses concepts related to ontogeny (development of an organism) and phylogeny (evolutionary history). It describes:
1) Epigenesis, the unfolding development of an organism from an egg or spore through differentiation of cells and formation of organs, in contrast to preformationist theories.
2) How altered ontogeny, such as changes in timing of developmental events (heterochrony), can produce phylogenetic changes through processes like neoteny or acceleration.
3) De Beer's eight ways that changes in ontogeny can lead to phylogenetic changes, simplified by Gould into changes in relative timing of developmental events.
This lecture provides an overview of DNA and uses HIV as a case study to demonstrate evolutionary concepts. It reviews that DNA is made up of nucleotides that pair in specific ways, and is located in the nucleus as well as mitochondria and chloroplasts. HIV exemplifies evolution through mutation and selection, as early drug treatments led to the rise of pre-existing resistant viral mutants. Phylogenetic analysis of HIV strains helps show transmission relationships and the historical origins of the virus in different primate species.
Introduction of Animal Genetics & History of GeneticsAashish Patel
This document provides an overview of genetics including key discoveries and scientists. It discusses Gregor Mendel's foundational work in 1866 and subsequent rediscovery of his principles. Important milestones are highlighted such as Watson and Crick's discovery of DNA structure in 1953. The document also covers branches of genetics, pre-Mendelian concepts of heredity, and applications of genetics in fields like taxonomy, veterinary medicine, and evolution.
This document provides an overview of key themes in biology, including:
1) Biology can be studied at different levels from molecules to ecosystems, and new properties emerge at each level.
2) Organisms interact with their environments, exchanging matter and energy.
3) Evolution accounts for the unity and diversity of life through common descent and natural selection.
Plants reproduce through both sexual and asexual reproduction. Sexual reproduction involves the fusion of male and female gametes to form seeds and fruits. Asexual reproduction occurs through vegetative structures like roots, stems, and leaves developing into new plants via methods such as cuttings, layering, and grafting. Genes contain DNA and are located on chromosomes, with humans having 23 chromosome pairs. DNA stores and transmits genetic information through the bases adenine, guanine, cytosine, and thymine bonding together to form structures like genes and chromosomes. Mutations can be inherited or acquired, and genetic engineering allows transferring genes between different species.
Plants can reproduce sexually through the fusion of male and female gametes to form seeds and fruits, or asexually through vegetative reproduction methods like cuttings, layering, grafting, and budding. Sexual reproduction mainly occurs in flowering plants. DNA contains genetic information stored as base pairs of adenine, guanine, cytosine, and thymine, and is packaged into chromosomes within the nucleus of cells. Genes transfer this genetic information through processes like replication, transcription, and translation. Mutations can occur through changes in DNA sequence, and genetic engineering allows transferring genes between different species.
This document provides an overview of a guest lecture on molecular genetics and population genetics. The lecture aims to review Mendelian genetics and its relationship to population genetics. It will introduce the field of population genetics and its significance. The lecturer, Hasan Alhaddad, will provide background on his education and research experience. He will review theories of inheritance pre-Mendel, Mendel's experiments with pea plants, evolution by natural selection, and how population genetics integrates genetics and evolution.
The document provides a timeline of the evolution of evolutionary thought from ancient Greek philosophers like Plato and Aristotle, through the 17th-18th centuries when naturalists began exploring new lands and observing similarities between species. It then covers the early 19th century work of Lamarck on inheritance of acquired traits and Darwin and Wallace's independent development of the theory of evolution by natural selection. The theory gained acceptance after Mendel's work on genetics supported Darwin's ideas about inheritance of traits. Later sections discuss evidence of evolution from fossils, comparative anatomy and embryology, biochemistry and molecular biology, and population genetics.
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.
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.pptHenry Hollis
The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
Confiscations, Kotahitanga, Kingitanga, Parliament, Suffrage, Repudiation, Economic Change, Agriculture, Gold Mining, Timber, Flax, Sheep, Dairying,
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.
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...TechSoup
Whether you're new to SEO or looking to refine your existing strategies, this webinar will provide you with actionable insights and practical tips to elevate your nonprofit's online presence.
A Free 200-Page eBook ~ Brain and Mind Exercise.pptxOH TEIK BIN
(A Free eBook comprising 3 Sets of Presentation of a selection of Puzzles, Brain Teasers and Thinking Problems to exercise both the mind and the Right and Left Brain. To help keep the mind and brain fit and healthy. Good for both the young and old alike.
Answers are given for all the puzzles and problems.)
With Metta,
Bro. Oh Teik Bin 🙏🤓🤔🥰
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...EduSkills OECD
Andreas Schleicher, Director of Education and Skills at the OECD presents at the launch of PISA 2022 Volume III - Creative Minds, Creative Schools on 18 June 2024.
Gender and Mental Health - Counselling and Family Therapy Applications and In...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
2. A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing
Introduction
Contents
The basic EC metaphor
Historical perspective
Biological inspiration:
– Darwinian evolution theory (simplified!)
– Genetics (simplified!)
Motivation for EC
What can EC do: examples of application
areas
3. A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing
Introduction
EVOLUTION
Environment
Individual
Fitness
The Main Evolutionary Computing
Metaphor
PROBLEM SOLVING
Problem
Candidate Solution
Quality
Quality chance for seeding new solutions
Fitness chances for survival and reproduction
4. A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing
Introduction
Brief History 1: the ancestors
• 1948, Turing:
proposes “genetical or evolutionary search”
• 1962, Bremermann
optimization through evolution and recombination
• 1964, Rechenberg
introduces evolution strategies
• 1965, L. Fogel, Owens and Walsh
introduce evolutionary programming
• 1975, Holland
introduces genetic algorithms
• 1992, Koza
introduces genetic programming
5. A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing
Introduction
Brief History 2: The rise of EC
• 1985: first international conference (ICGA)
• 1990: first international conference in Europe (PPSN)
• 1993: first scientific EC journal (MIT Press)
• 1997: launch of European EC Research Network EvoNet
6. A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing
Introduction
EC in the early 21st Century
• 3 major EC conferences, about 10 small related ones
• 3 scientific core EC journals
• 750-1000 papers published in 2003
• numerous applications
• numerous consultancy and R&D firms
7. A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing
Introduction
Darwinian Evolution 1:
Survival of the fittest
All environments have finite resources
(i.e., can only support a limited number of individuals)
Life forms have basic instinct/ lifecycles geared towards
reproduction
Therefore some kind of selection is inevitable
Those individuals that compete for the resources most
effectively have increased chance of reproduction
Note: fitness in natural evolution is a derived, secondary
measure, i.e., we (humans) assign a high fitness to
individuals with many offspring
8. A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing
Introduction
Darwinian Evolution 2:
Diversity drives change
Phenotypic traits:
– Behavior / physical differences that affect response to
environment
– Partly determined by inheritance, partly by factors during
development
– Unique to each individual, partly as a result of random
changes
If phenotypic traits:
– Lead to higher chances of reproduction
– Can be inherited
then they will tend to increase in subsequent
generations,
leading to new combinations of traits …
9. A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing
Introduction
Darwinian Evolution:Summary
Population consists of diverse set of individuals
Combinations of traits that are better adapted tend to
increase representation in population
Individuals are “units of selection”
Variations occur through random changes yielding
constant source of diversity, coupled with selection
means that:
Population is the “unit of evolution”
Note the absence of “guiding force”
10. A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing
Introduction
Adaptive landscape metaphor (Wright, 1932)
• Can view a population with n traits as existing in a n+1-
dimensional space (landscape) with height
corresponding to fitness
• Each different individual (phenotype) represents a
single point on the landscape
• Population is therefore a “cloud” of points, moving on
the landscape over time as it evolves - adaptation
11. A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing
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Example with two traits
12. A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing
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Adaptive landscape metaphor (cont’d)
•Selection “pushes” population up the landscape
•Genetic drift:
• random variations in feature distribution
(+ or -) arising from sampling error
• can cause the population “melt down” hills, thus
crossing valleys and leaving local optima (or
alternative global optima!)
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Natural Genetics
The information required to build a living organism is
coded in the DNA of that organism
Genotype (DNA inside) determines phenotype (outside)
[Genes phenotypic traits] is a complex mapping
– One gene may affect many traits (pleiotropy)
– Many genes may affect one trait (polygeny)
Causality: Small changes in the genotype lead to small
changes in the organism (e.g., height, hair color)
Epistases: The effect of one gene on phenotype
depends on the values of other genes (opposite is
orthogonality)
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Genes and the Genome
Genes are encoded in strands of DNA called
chromosomes
In most cells, there are two (homologous) copies of
each chromosome (diploidy)
The complete genetic material in an individual’s
genotype is called the Genome
Within a species, most of the genetic material is the
same
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Example: Homo Sapiens
Human DNA is organized into chromosomes
Most human body cells contain 23 pairs of
chromosomes which together define the physical
attributes of the individual:
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Reproductive Cells
Gametes (sperm and egg cells) contain 23 individual
chromosomes rather than 23 pairs
Cells with only one copy of each chromosome are
called Haploid
Gametes are formed by a special form of cell splitting
called meiosis
During meiosis the pairs of chromosomes undergo an
operation called crossing-over
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Crossing-over during meiosis
Chromosome pairs align and duplicate
Inner pairs link at a centromere and swap parts of
themselves
Outcome is one copy of maternal/paternal
chromosome plus two entirely new combinations
After crossing-over one of each pair goes into each
gamete
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Fertilization
Sperm cell from Father Egg cell from Mother
New person cell (zygote)
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After fertilization
New zygote rapidly divides creating many cells all with
the same genetic contents
Although all cells contain the same genes, depending
on, for example where they are in the organism, they
will behave differently
This process of differential behavior during
development is called ontogenesis
All of this uses, and is controlled by, the same
mechanism for decoding the genes in DNA
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Genetic code
• All proteins in life on earth are composed of sequences
built from 20 different amino acids
• DNA is built from four nucleotides in a double helix
spiral: purines A,G; pyrimidines T,C
• Triplets of these form codons, each of which codes for
a specific amino acid
• Much redundancy:
• purines complement pyrimidines
• the DNA contains much rubbish
• 43=64 codons code for 20 amino acids
• genetic code = the mapping from codons to amino acids
• For all natural life on earth, the genetic code is the
same !
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A central claim in molecular genetics: only one way flow
Genotype Phenotype
Genotype Phenotype
Lamarckism (saying that acquired features can be
inherited) is thus wrong!
Transcription, translation
22. A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing
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Mutation
Occasionally some of the genetic material changes
very slightly during this process (replication error)
This means that the child might have genetic material
information not inherited from either parent
This can be
– catastrophic: offspring in not viable (most likely)
– neutral: new feature does not influence fitness
– advantageous: strong new feature occurs
Redundancy in the genetic code forms a good way of
error prevention
23. A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing
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Motivations for EC: 1
Nature has always served as a source of inspiration
for engineers and scientists
The best problem solver known in nature is:
– the (human) brain that created “the wheel, New
York, wars and so on” (after Douglas Adams’ Hitch-
Hikers Guide)
– the evolution mechanism that created the human
brain (after Darwin’s Origin of Species)
Answer 1 neurocomputing
Answer 2 evolutionary computing
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Motivations for EC: 2
• Developing, analyzing, applying problem solving
methods a.k.a. algorithms is a central theme in
mathematics and computer science
• Time for thorough problem analysis decreases
• Complexity of problems to be solved increases
• Consequence:
Robust problem solving technology needed
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Problem type 1 : Optimization
We have a model of our system and seek inputs that
give us a specified goal
e.g.
– time tables for university, or hospital
– design specifications, etc.
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Optimization example 1: University timetabling
Enormously big search space
Timetables must be good
“Good” is defined by a number of competing criteria
Timetables must be feasible
Vast majority of search space is infeasible
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Optimization example 2: Satellite structure
Optimized satellite designs for
NASA to maximize vibration
isolation
Evolving: design structures
Fitness: vibration resistance
Evolutionary “creativity”
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Problem types 2: Modeling
We have corresponding sets of inputs & outputs and
seek a model that delivers the correct output for every
known input
• Evolutionary machine learning
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Modelling example: loan applicant creditibility
British bank evolved
creditability model to predict
loan paying behavior of new
applicants
Evolving: prediction models
Fitness: model accuracy on
historical data
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Problem type 3: Simulation
We have a given model and wish to know the outputs
that arise under different input conditions
Often used to answer “what-if” questions in evolving
dynamic environments
e.g. Evolutionary economics, Artificial Life
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Simulation example: evolving
artificial societies
Simulating trade, economic
competition, etc. to calibrate
models
Use models to optimize
strategies and policies
Evolutionary economy
Survival of the fittest is universal
(big/small fish)
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Incest prevention keeps evolution from rapid degeneration
(we knew this)
Multi-parent reproduction, makes evolution more efficient
(this does not exist on Earth in carbon!)
Simulation example 2: biological
interpretations