António Manso Luís Correia

Size: px
Start display at page:

Download "António Manso Luís Correia"

Transcription

1 António Manso Luís Correia

2 Populations, Multisets and MuGA SMuGA- Symbiogenetic MuGA Hosts and parasites Diversity guided parasite evolution Experimental results Conclusions

3 GA uses collections of individuals of individuals that evolve together Good individuals gain clones Clones decreases genetic diversity Change population representation Replace collection with multiset. Individual Fitness Population of OnesMax

4 Simple Population Individual Fitness MultiIndividual Fitness < 3, > < 2, > < 2, > < 1, > Mutiset Population Fixed dimension of MultiPopulation Controls the genetic diversity at genotype level Number of copies May be used for selection pressure

5 Start Selection MP 1 Crossover MP2 Mutation MP0 Replacement MP3 Control of the number of copies Rescaling MP4 Replacement

6 May use adapted genetic operators that explore multiset representation Multiset Selection Mutliset Crossover Multiset Mutation Multiset Replacement Adaptive Decimation Successfully applied to solve difficult problems Bitstring coded. Real coded.

7 Solving problems with large genomes

8 Species evolve together!

9 Two populations Hosts solutions of the problem Parasites partial solutions In symbiotic relationship Parasites discover good genes Parasites transfer genes to Hosts Hosts better reward parasites with good genes

10 Two phases Algorithm Evolution in isolation Survival of the fittest Collaboration Parasites infects hosts Parasites... Parasites... Parasites Evaluate Evaluate Evaluate Collaboration Collaboration Evaluate Evaluate Hosts... Hosts Hosts... Hosts Hosts

11 Tuple < position, genome> Collaboration Transference of genetic material of parasites to host Position Host * * * * * * * * * * Parasite Parasite Collaboration 0 1 * * 1 1 * * 0 1

12 Enables parasite length change a) p c) p p p Cut Point Cut Point b) o d) o o

13 Includes change of genome size Uses gene diversity information

14 Compute ocupation space of parasite population. Move parasites to less populated regions Position I I O O Parasite Population I O I I I O O O I I I I O I O O O Empty Slots Position prob. 0,21 0,16 0,11 0,11 0 0,11 0 0,05 0,11 0,16

15 Probability of break is computed by the sigmoid function Allows growth of small parasites Avoid overfit of parasites to hosts break probability 1,0 0,9 0,8 0,7 0,6 0,5 t= - 6 s= 12 0,4 0,3 t= - 6 s= 24 0,2 0,1 0,0 0,00 0,25 0,50 0,75 1,00 x = parasite size / host size

16 Compute the diversity of gene values in the parasite population Use that probability to change allele value Position I I I O Parasite Population I O I I I O O O I I I I O I O O O Prob. of bit "0" 0 0 0,5 0, , Prob. of bit "1" 0 1 0,5 0, ,

17 Heuristic Evaluation Rank individuals in host population [N 1] == [ Best... Worst ] Shift Ranks by N/ 2 [ N/2 - N/2-1] Sum ranks of the individuals that contain parasite If parasite is new: discovery reward ( > 0 ) Multiply the sum of ranks by gene diversity Beneficial and new parasites more rewarded

18 ParasiteEvolution (ppop, hpop, k) selectpop = select k parasites from ppop offspringpop = recombine selectpop while offspringpop.size < ppop.size Select random parasite from offspringpop Mutate a clone of parasite Insert mutated clone in offspringpop End while Evaluate offspringpop in hpop ppop = offspringpop End Function.

19 Functions with 8 bits Trap Intertwined Trap Trap(x) x Massively Multimodal Deceptive Problem Trap01(x) Trap 0 Trap x Concatenated functions 64, 128, 256, 512, 1024 (bits) MMDP(x) x

20 Host population size : 64 Parasite population size : 128 Iterations in isolation : 32 Evolution performed with adapted multiset genetic operators 32 independent runs

21

22

23

24 Mean number of evaluations to reach the optimum Chromossome length (bits) Trap pattern Trap01 MMDP Mean Std Mean Std Mean Std 64 5, , , , , , , , , , , , , , , , , , , , , , , , , , , , ,577, ,051.77

25 Diversity is used to guide genetic operators applied to parasites At gene level At population level (parasite position) Hosts incorporate genes discovered by parasites Efficient symbiotic model between hosts and parasites enables optimization of problems with large genomes

26

Chapter 8: Introduction to Evolutionary Computation

Chapter 8: Introduction to Evolutionary Computation Computational Intelligence: Second Edition Contents Some Theories about Evolution Evolution is an optimization process: the aim is to improve the ability of an organism to survive in dynamically changing

More information

CSC 4510 Machine Learning

CSC 4510 Machine Learning 10: Gene(c Algorithms CSC 4510 Machine Learning Dr. Mary Angela Papalaskari Department of CompuBng Sciences Villanova University Course website: www.csc.villanova.edu/~map/4510/ Slides of this presenta(on

More information

Lecture 9 Evolutionary Computation: Genetic algorithms

Lecture 9 Evolutionary Computation: Genetic algorithms Lecture 9 Evolutionary Computation: Genetic algorithms Introduction, or can evolution be intelligent? Simulation of natural evolution Genetic algorithms Case study: maintenance scheduling with genetic

More information

The Story So Far... The central problem of this course: Smartness( X ) arg max X. Possibly with some constraints on X.

The Story So Far... The central problem of this course: Smartness( X ) arg max X. Possibly with some constraints on X. Heuristic Search The Story So Far... The central problem of this course: arg max X Smartness( X ) Possibly with some constraints on X. (Alternatively: arg min Stupidness(X ) ) X Properties of Smartness(X)

More information

Evolutionary Computation. DEIS-Cesena Alma Mater Studiorum Università di Bologna Cesena (Italia)

Evolutionary Computation. DEIS-Cesena Alma Mater Studiorum Università di Bologna Cesena (Italia) Evolutionary Computation DEIS-Cesena Alma Mater Studiorum Università di Bologna Cesena (Italia) andrea.roli@unibo.it Evolutionary Computation Inspiring principle: theory of natural selection Species face

More information

Genetic Algorithms & Modeling

Genetic Algorithms & Modeling Genetic Algorithms & Modeling : Soft Computing Course Lecture 37 40, notes, slides www.myreaders.info/, RC Chakraborty, e-mail rcchak@gmail.com, Aug. 10, 2010 http://www.myreaders.info/html/soft_computing.html

More information

7.2: Natural Selection and Artificial Selection pg

7.2: Natural Selection and Artificial Selection pg 7.2: Natural Selection and Artificial Selection pg. 305-311 Key Terms: natural selection, selective pressure, fitness, artificial selection, biotechnology, and monoculture. Natural Selection is the process

More information

Fundamentals of Genetic Algorithms

Fundamentals of Genetic Algorithms Fundamentals of Genetic Algorithms : AI Course Lecture 39 40, notes, slides www.myreaders.info/, RC Chakraborty, e-mail rcchak@gmail.com, June 01, 2010 www.myreaders.info/html/artificial_intelligence.html

More information

Curriculum Links. AQA GCE Biology. AS level

Curriculum Links. AQA GCE Biology. AS level Curriculum Links AQA GCE Biology Unit 2 BIOL2 The variety of living organisms 3.2.1 Living organisms vary and this variation is influenced by genetic and environmental factors Causes of variation 3.2.2

More information

Computational statistics

Computational statistics Computational statistics Combinatorial optimization Thierry Denœux February 2017 Thierry Denœux Computational statistics February 2017 1 / 37 Combinatorial optimization Assume we seek the maximum of f

More information

Intelligens Számítási Módszerek Genetikus algoritmusok, gradiens mentes optimálási módszerek

Intelligens Számítási Módszerek Genetikus algoritmusok, gradiens mentes optimálási módszerek Intelligens Számítási Módszerek Genetikus algoritmusok, gradiens mentes optimálási módszerek 2005/2006. tanév, II. félév Dr. Kovács Szilveszter E-mail: szkovacs@iit.uni-miskolc.hu Informatikai Intézet

More information

Evolutionary computation

Evolutionary computation Evolutionary computation Andrea Roli andrea.roli@unibo.it DEIS Alma Mater Studiorum Università di Bologna Evolutionary computation p. 1 Evolutionary Computation Evolutionary computation p. 2 Evolutionary

More information

Molecular Evolution & the Origin of Variation

Molecular Evolution & the Origin of Variation Molecular Evolution & the Origin of Variation What Is Molecular Evolution? Molecular evolution differs from phenotypic evolution in that mutations and genetic drift are much more important determinants

More information

Molecular Evolution & the Origin of Variation

Molecular Evolution & the Origin of Variation Molecular Evolution & the Origin of Variation What Is Molecular Evolution? Molecular evolution differs from phenotypic evolution in that mutations and genetic drift are much more important determinants

More information

NOTES Ch 17: Genes and. Variation

NOTES Ch 17: Genes and. Variation NOTES Ch 17: Genes and Vocabulary Fitness Genetic Drift Punctuated Equilibrium Gene flow Adaptive radiation Divergent evolution Convergent evolution Gradualism Variation 17.1 Genes & Variation Darwin developed

More information

Haploid & diploid recombination and their evolutionary impact

Haploid & diploid recombination and their evolutionary impact Haploid & diploid recombination and their evolutionary impact W. Garrett Mitchener College of Charleston Mathematics Department MitchenerG@cofc.edu http://mitchenerg.people.cofc.edu Introduction The basis

More information

how should the GA proceed?

how should the GA proceed? how should the GA proceed? string fitness 10111 10 01000 5 11010 3 00011 20 which new string would be better than any of the above? (the GA does not know the mapping between strings and fitness values!)

More information

[Read Chapter 9] [Exercises 9.1, 9.2, 9.3, 9.4]

[Read Chapter 9] [Exercises 9.1, 9.2, 9.3, 9.4] 1 EVOLUTIONARY ALGORITHMS [Read Chapter 9] [Exercises 9.1, 9.2, 9.3, 9.4] Evolutionary computation Prototypical GA An example: GABIL Schema theorem Genetic Programming Individual learning and population

More information

Crossover Techniques in GAs

Crossover Techniques in GAs Crossover Techniques in GAs Debasis Samanta Indian Institute of Technology Kharagpur dsamanta@iitkgp.ac.in 16.03.2018 Debasis Samanta (IIT Kharagpur) Soft Computing Applications 16.03.2018 1 / 1 Important

More information

Search. Search is a key component of intelligent problem solving. Get closer to the goal if time is not enough

Search. Search is a key component of intelligent problem solving. Get closer to the goal if time is not enough Search Search is a key component of intelligent problem solving Search can be used to Find a desired goal if time allows Get closer to the goal if time is not enough section 11 page 1 The size of the search

More information

Evolutionary Robotics

Evolutionary Robotics Evolutionary Robotics Previously on evolutionary robotics Evolving Neural Networks How do we evolve a neural network? Evolving Neural Networks How do we evolve a neural network? One option: evolve the

More information

Lecture 22. Introduction to Genetic Algorithms

Lecture 22. Introduction to Genetic Algorithms Lecture 22 Introduction to Genetic Algorithms Thursday 14 November 2002 William H. Hsu, KSU http://www.kddresearch.org http://www.cis.ksu.edu/~bhsu Readings: Sections 9.1-9.4, Mitchell Chapter 1, Sections

More information

Mechanisms of Evolution Microevolution. Key Concepts. Population Genetics

Mechanisms of Evolution Microevolution. Key Concepts. Population Genetics Mechanisms of Evolution Microevolution Population Genetics Key Concepts 23.1: Population genetics provides a foundation for studying evolution 23.2: Mutation and sexual recombination produce the variation

More information

5/31/2012. Speciation and macroevolution - Chapter

5/31/2012. Speciation and macroevolution - Chapter Speciation and macroevolution - Chapter Objectives: - Review meiosis -Species -Repro. Isolating mechanisms - Speciation -Is evolution always slow -Extinction How Are Populations, Genes, And Evolution Related?

More information

CHAPTER 23 THE EVOLUTIONS OF POPULATIONS. Section C: Genetic Variation, the Substrate for Natural Selection

CHAPTER 23 THE EVOLUTIONS OF POPULATIONS. Section C: Genetic Variation, the Substrate for Natural Selection CHAPTER 23 THE EVOLUTIONS OF POPULATIONS Section C: Genetic Variation, the Substrate for Natural Selection 1. Genetic variation occurs within and between populations 2. Mutation and sexual recombination

More information

What is Natural Selection? Natural & Artificial Selection. Answer: Answer: What are Directional, Stabilizing, Disruptive Natural Selection?

What is Natural Selection? Natural & Artificial Selection. Answer: Answer: What are Directional, Stabilizing, Disruptive Natural Selection? What is Natural Selection? Natural & Artificial Selection Practice Quiz What are Directional, Stabilizing, Disruptive Natural Selection? When an environment selects for a trait in organisms. Who came up

More information

NEUROEVOLUTION. Contents. Evolutionary Computation. Neuroevolution. Types of neuro-evolution algorithms

NEUROEVOLUTION. Contents. Evolutionary Computation. Neuroevolution. Types of neuro-evolution algorithms Contents Evolutionary Computation overview NEUROEVOLUTION Presenter: Vlad Chiriacescu Neuroevolution overview Issues in standard Evolutionary Computation NEAT method Complexification in competitive coevolution

More information

Adaptive Generalized Crowding for Genetic Algorithms

Adaptive Generalized Crowding for Genetic Algorithms Carnegie Mellon University From the SelectedWorks of Ole J Mengshoel Fall 24 Adaptive Generalized Crowding for Genetic Algorithms Ole J Mengshoel, Carnegie Mellon University Severinio Galan Antonio de

More information

V. Evolutionary Computing. Read Flake, ch. 20. Assumptions. Genetic Algorithms. Fitness-Biased Selection. Outline of Simplified GA

V. Evolutionary Computing. Read Flake, ch. 20. Assumptions. Genetic Algorithms. Fitness-Biased Selection. Outline of Simplified GA Part 5A: Genetic Algorithms V. Evolutionary Computing A. Genetic Algorithms Read Flake, ch. 20 1 2 Genetic Algorithms Developed by John Holland in 60s Did not become popular until late 80s A simplified

More information

V. Evolutionary Computing. Read Flake, ch. 20. Genetic Algorithms. Part 5A: Genetic Algorithms 4/10/17. A. Genetic Algorithms

V. Evolutionary Computing. Read Flake, ch. 20. Genetic Algorithms. Part 5A: Genetic Algorithms 4/10/17. A. Genetic Algorithms V. Evolutionary Computing A. Genetic Algorithms 4/10/17 1 Read Flake, ch. 20 4/10/17 2 Genetic Algorithms Developed by John Holland in 60s Did not become popular until late 80s A simplified model of genetics

More information

Estimation-of-Distribution Algorithms. Discrete Domain.

Estimation-of-Distribution Algorithms. Discrete Domain. Estimation-of-Distribution Algorithms. Discrete Domain. Petr Pošík Introduction to EDAs 2 Genetic Algorithms and Epistasis.....................................................................................

More information

IV. Evolutionary Computing. Read Flake, ch. 20. Assumptions. Genetic Algorithms. Fitness-Biased Selection. Outline of Simplified GA

IV. Evolutionary Computing. Read Flake, ch. 20. Assumptions. Genetic Algorithms. Fitness-Biased Selection. Outline of Simplified GA IV. Evolutionary Computing A. Genetic Algorithms Read Flake, ch. 20 2014/2/26 1 2014/2/26 2 Genetic Algorithms Developed by John Holland in 60s Did not become popular until late 80s A simplified model

More information

Grade 11 Biology SBI3U 12

Grade 11 Biology SBI3U 12 Grade 11 Biology SBI3U 12 } We ve looked at Darwin, selection, and evidence for evolution } We can t consider evolution without looking at another branch of biology: } Genetics } Around the same time Darwin

More information

Scaling Up. So far, we have considered methods that systematically explore the full search space, possibly using principled pruning (A* etc.).

Scaling Up. So far, we have considered methods that systematically explore the full search space, possibly using principled pruning (A* etc.). Local Search Scaling Up So far, we have considered methods that systematically explore the full search space, possibly using principled pruning (A* etc.). The current best such algorithms (RBFS / SMA*)

More information

Lecture 15: Genetic Algorithms

Lecture 15: Genetic Algorithms Lecture 15: Genetic Algorithms Dr Roman V Belavkin BIS3226 Contents 1 Combinatorial Problems 1 2 Natural Selection 2 3 Genetic Algorithms 3 31 Individuals and Population 3 32 Fitness Functions 3 33 Encoding

More information

State of the art in genetic algorithms' research

State of the art in genetic algorithms' research State of the art in genetic algorithms' Genetic Algorithms research Prabhas Chongstitvatana Department of computer engineering Chulalongkorn university A class of probabilistic search, inspired by natural

More information

Evolution by Natural Selection

Evolution by Natural Selection Evolution by Natural Selection What is evolution? What is evolution? The change in the genetic makeup of a population over time (narrowly defined) Evolution accounts for the diversity of life on Earth

More information

Positively versus Negatively Frequency-Dependent Selection

Positively versus Negatively Frequency-Dependent Selection Positively versus Negatively Frequency-Dependent Selection Robert Morris and Tim Watson Faculty of Technology, De Montfort University, Leicester, United Kingdom, LE1 9BH robert.morris@email.dmu.ac.uk Abstract.

More information

Evolutionary Computation

Evolutionary Computation Evolutionary Computation - Computational procedures patterned after biological evolution. - Search procedure that probabilistically applies search operators to set of points in the search space. - Lamarck

More information

1. T/F: Genetic variation leads to evolution. 2. What is genetic equilibrium? 3. What is speciation? How does it occur?

1. T/F: Genetic variation leads to evolution. 2. What is genetic equilibrium? 3. What is speciation? How does it occur? 1. T/F: Genetic variation leads to evolution. 2. What is genetic equilibrium? 3. What is speciation? How does it occur? Warm UP Notes on Environmental Factor Concept Map Brief 6 questions and Concept Map

More information

NOTES CH 17 Evolution of. Populations

NOTES CH 17 Evolution of. Populations NOTES CH 17 Evolution of Vocabulary Fitness Genetic Drift Punctuated Equilibrium Gene flow Adaptive radiation Divergent evolution Convergent evolution Gradualism Populations 17.1 Genes & Variation Darwin

More information

Application Evolution: Part 1.1 Basics of Coevolution Dynamics

Application Evolution: Part 1.1 Basics of Coevolution Dynamics Application Evolution: Part 1.1 Basics of Coevolution Dynamics S. chilense S. peruvianum Summer Semester 2013 Prof Aurélien Tellier FG Populationsgenetik Color code Color code: Red = Important result or

More information

Research Article Effect of Population Structures on Quantum-Inspired Evolutionary Algorithm

Research Article Effect of Population Structures on Quantum-Inspired Evolutionary Algorithm Applied Computational Intelligence and So Computing, Article ID 976202, 22 pages http://dx.doi.org/10.1155/2014/976202 Research Article Effect of Population Structures on Quantum-Inspired Evolutionary

More information

A. Correct! Genetically a female is XX, and has 22 pairs of autosomes.

A. Correct! Genetically a female is XX, and has 22 pairs of autosomes. MCAT Biology - Problem Drill 08: Meiosis and Genetic Variability Question No. 1 of 10 1. A human female has pairs of autosomes and her sex chromosomes are. Question #01 (A) 22, XX. (B) 23, X. (C) 23, XX.

More information

APES C4L2 HOW DOES THE EARTH S LIFE CHANGE OVER TIME? Textbook pages 85 88

APES C4L2 HOW DOES THE EARTH S LIFE CHANGE OVER TIME? Textbook pages 85 88 APES C4L2 HOW DOES THE EARTH S LIFE CHANGE OVER TIME? Textbook pages 85 88 Big Ideas Concept 4-2A The scientific theory of evolution explains how life on Earth changes over time through changes in the

More information

Genetical theory of natural selection

Genetical theory of natural selection Reminders Genetical theory of natural selection Chapter 12 Natural selection evolution Natural selection evolution by natural selection Natural selection can have no effect unless phenotypes differ in

More information

9 Genetic diversity and adaptation Support. AQA Biology. Genetic diversity and adaptation. Specification reference. Learning objectives.

9 Genetic diversity and adaptation Support. AQA Biology. Genetic diversity and adaptation. Specification reference. Learning objectives. Genetic diversity and adaptation Specification reference 3.4.3 3.4.4 Learning objectives After completing this worksheet you should be able to: understand how meiosis produces haploid gametes know how

More information

DETECTING THE FAULT FROM SPECTROGRAMS BY USING GENETIC ALGORITHM TECHNIQUES

DETECTING THE FAULT FROM SPECTROGRAMS BY USING GENETIC ALGORITHM TECHNIQUES DETECTING THE FAULT FROM SPECTROGRAMS BY USING GENETIC ALGORITHM TECHNIQUES Amin A. E. 1, El-Geheni A. S. 2, and El-Hawary I. A **. El-Beali R. A. 3 1 Mansoura University, Textile Department 2 Prof. Dr.

More information

Evolution (Chapters 15 & 16)

Evolution (Chapters 15 & 16) Evolution (Chapters 15 & 16) Before You Read... Use the What I Know column to list the things you know about evolution. Then list the questions you have about evolution in the What I Want to Find Out column.

More information

Genetic Engineering and Creative Design

Genetic Engineering and Creative Design Genetic Engineering and Creative Design Background genes, genotype, phenotype, fitness Connecting genes to performance in fitness Emergent gene clusters evolved genes MIT Class 4.208 Spring 2002 Evolution

More information

Evolutionary Algorithms: Introduction. Department of Cybernetics, CTU Prague.

Evolutionary Algorithms: Introduction. Department of Cybernetics, CTU Prague. Evolutionary Algorithms: duction Jiří Kubaĺık Department of Cybernetics, CTU Prague http://cw.felk.cvut.cz/doku.php/courses/a4m33bia/start pcontents 1. duction to Evolutionary Algorithms (EAs) Pioneers

More information

Problems for 3505 (2011)

Problems for 3505 (2011) Problems for 505 (2011) 1. In the simplex of genotype distributions x + y + z = 1, for two alleles, the Hardy- Weinberg distributions x = p 2, y = 2pq, z = q 2 (p + q = 1) are characterized by y 2 = 4xz.

More information

Darwinian Selection. Chapter 6 Natural Selection Basics 3/25/13. v evolution vs. natural selection? v evolution. v natural selection

Darwinian Selection. Chapter 6 Natural Selection Basics 3/25/13. v evolution vs. natural selection? v evolution. v natural selection Chapter 6 Natural Selection Basics Natural Selection Haploid Diploid, Sexual Results for a Diallelic Locus Fisher s Fundamental Theorem Darwinian Selection v evolution vs. natural selection? v evolution

More information

Stochastic Search: Part 2. Genetic Algorithms. Vincent A. Cicirello. Robotics Institute. Carnegie Mellon University

Stochastic Search: Part 2. Genetic Algorithms. Vincent A. Cicirello. Robotics Institute. Carnegie Mellon University Stochastic Search: Part 2 Genetic Algorithms Vincent A. Cicirello Robotics Institute Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA 15213 cicirello@ri.cmu.edu 1 The Genetic Algorithm (GA)

More information

Bell Question 9/13 What is a cell?

Bell Question 9/13 What is a cell? Bell Question 9/13 What is a cell? So you missed your test yesterday. You need to come in and take it before or after school or at lunch. Retakes..must be done on your own time and finished by Wednesday

More information

Dynamic Optimization using Self-Adaptive Differential Evolution

Dynamic Optimization using Self-Adaptive Differential Evolution Dynamic Optimization using Self-Adaptive Differential Evolution IEEE Congress on Evolutionary Computation (IEEE CEC 2009), Trondheim, Norway, May 18-21, 2009 J. Brest, A. Zamuda, B. Bošković, M. S. Maučec,

More information

Objective 3.01 (DNA, RNA and Protein Synthesis)

Objective 3.01 (DNA, RNA and Protein Synthesis) Objective 3.01 (DNA, RNA and Protein Synthesis) DNA Structure o Discovered by Watson and Crick o Double-stranded o Shape is a double helix (twisted ladder) o Made of chains of nucleotides: o Has four types

More information

1.5.1 ESTIMATION OF HAPLOTYPE FREQUENCIES:

1.5.1 ESTIMATION OF HAPLOTYPE FREQUENCIES: .5. ESTIMATION OF HAPLOTYPE FREQUENCIES: Chapter - 8 For SNPs, alleles A j,b j at locus j there are 4 haplotypes: A A, A B, B A and B B frequencies q,q,q 3,q 4. Assume HWE at haplotype level. Only the

More information

Q2 (4.6) Put the following in order from biggest to smallest: Gene DNA Cell Chromosome Nucleus. Q8 (Biology) (4.6)

Q2 (4.6) Put the following in order from biggest to smallest: Gene DNA Cell Chromosome Nucleus. Q8 (Biology) (4.6) Q1 (4.6) What is variation? Q2 (4.6) Put the following in order from biggest to smallest: Gene DNA Cell Chromosome Nucleus Q3 (4.6) What are genes? Q4 (4.6) What sort of reproduction produces genetically

More information

Chapter 02 Population Genetics

Chapter 02 Population Genetics Chapter 02 Population Genetics Multiple Choice Questions 1. The first person to publish a theory that species change over time was A. Plato B. Lamarck C. Darwin D. Wallace E. Mendel 2. Charles Robert Darwin

More information

Population Genetics: a tutorial

Population Genetics: a tutorial : a tutorial Institute for Science and Technology Austria ThRaSh 2014 provides the basic mathematical foundation of evolutionary theory allows a better understanding of experiments allows the development

More information

BIOL Evolution. Lecture 9

BIOL Evolution. Lecture 9 BIOL 432 - Evolution Lecture 9 J Krause et al. Nature 000, 1-4 (2010) doi:10.1038/nature08976 Selection http://www.youtube.com/watch?v=a38k mj0amhc&feature=playlist&p=61e033 F110013706&index=0&playnext=1

More information

BIG IDEA 4: BIOLOGICAL SYSTEMS INTERACT, AND THESE SYSTEMS AND THEIR INTERACTIONS POSSESS COMPLEX PROPERTIES.

BIG IDEA 4: BIOLOGICAL SYSTEMS INTERACT, AND THESE SYSTEMS AND THEIR INTERACTIONS POSSESS COMPLEX PROPERTIES. Enduring Understanding 4.C Independent Study Assignment Assignment Instructions Both components of this assignment (Part I and Part II) should be completed on the pages provided. Each numbered component

More information

Biology 11 UNIT 1: EVOLUTION LESSON 2: HOW EVOLUTION?? (MICRO-EVOLUTION AND POPULATIONS)

Biology 11 UNIT 1: EVOLUTION LESSON 2: HOW EVOLUTION?? (MICRO-EVOLUTION AND POPULATIONS) Biology 11 UNIT 1: EVOLUTION LESSON 2: HOW EVOLUTION?? (MICRO-EVOLUTION AND POPULATIONS) Objectives: By the end of the lesson you should be able to: Describe the 2 types of evolution Describe the 5 ways

More information

Q Expected Coverage Achievement Merit Excellence. Punnett square completed with correct gametes and F2.

Q Expected Coverage Achievement Merit Excellence. Punnett square completed with correct gametes and F2. NCEA Level 2 Biology (91157) 2018 page 1 of 6 Assessment Schedule 2018 Biology: Demonstrate understanding of genetic variation and change (91157) Evidence Q Expected Coverage Achievement Merit Excellence

More information

A Discrete Artificial Regulatory Network for Simulating the Evolution of Computation

A Discrete Artificial Regulatory Network for Simulating the Evolution of Computation A Discrete Artificial Regulatory Network for Simulating the Evolution of Computation W. Garrett Mitchener College of Charleston Mathematics Department July, 22 http://mitchenerg.people.cofc.edu mitchenerg@cofc.edu

More information

Neutral Theory of Molecular Evolution

Neutral Theory of Molecular Evolution Neutral Theory of Molecular Evolution Kimura Nature (968) 7:64-66 King and Jukes Science (969) 64:788-798 (Non-Darwinian Evolution) Neutral Theory of Molecular Evolution Describes the source of variation

More information

Short Answers Worksheet Grade 6

Short Answers Worksheet Grade 6 Short Answers Worksheet Grade 6 Short Answer 1. What is the role of the nucleolus? 2. What are the two different kinds of endoplasmic reticulum? 3. Name three cell parts that help defend the cell against

More information

Gender Separation and Mating Constraints

Gender Separation and Mating Constraints Gender Separation and Mating Constraints Dana Vrajitoru danav@cs.iusb.edu Intelligent Systems Laboratory, Computer and Information Sciences Department, Indiana University South Bend, 1700 Mishawaka Ave,

More information

THE PROBLEM OF locating all the optima within a fitness

THE PROBLEM OF locating all the optima within a fitness IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, VOL. 11, NO. 4, AUGUST 2007 453 Where Are the Niches? Dynamic Fitness Sharing Antonio Della Cioppa, Member, IEEE, Claudio De Stefano, and Angelo Marcelli,

More information

Processes of Evolution

Processes of Evolution 15 Processes of Evolution Forces of Evolution Concept 15.4 Selection Can Be Stabilizing, Directional, or Disruptive Natural selection can act on quantitative traits in three ways: Stabilizing selection

More information

Genetically Generated Neural Networks II: Searching for an Optimal Representation

Genetically Generated Neural Networks II: Searching for an Optimal Representation Boston University OpenBU Cognitive & Neural Systems http://open.bu.edu CAS/CNS Technical Reports 1992-02 Genetically Generated Neural Networks II: Searching for an Optimal Representation Marti, Leonardo

More information

The European Applied Business Research Conference Rothenburg, Germany 2002

The European Applied Business Research Conference Rothenburg, Germany 2002 The European Applied Business Research Conference Rothenburg, Germany 00 Using Genetic Algorithms To Optimise Kanban-Based Production System M Al-Tahat, (E-mail: mohammadal-tahat@mailinguniboit), University

More information

Development. biologically-inspired computing. lecture 16. Informatics luis rocha x x x. Syntactic Operations. biologically Inspired computing

Development. biologically-inspired computing. lecture 16. Informatics luis rocha x x x. Syntactic Operations. biologically Inspired computing lecture 16 -inspired S S2 n p!!! 1 S Syntactic Operations al Code:N Development x x x 1 2 n p S Sections I485/H400 course outlook Assignments: 35% Students will complete 4/5 assignments based on algorithms

More information

A) oldest on bottom layer, youngest on top. B) the type of environment it was

A) oldest on bottom layer, youngest on top. B) the type of environment it was Test date: BAT list: Evolution Chapters 10 & 11 Name: Evolution Unit Vocabulary Convergent evolution Evolution Divergent evolution Embryology Biogeography Genetic drift Gradualism Charles Darwin Natural

More information

I N N O V A T I O N L E C T U R E S (I N N O l E C) Petr Kuzmič, Ph.D. BioKin, Ltd. WATERTOWN, MASSACHUSETTS, U.S.A.

I N N O V A T I O N L E C T U R E S (I N N O l E C) Petr Kuzmič, Ph.D. BioKin, Ltd. WATERTOWN, MASSACHUSETTS, U.S.A. I N N O V A T I O N L E C T U R E S (I N N O l E C) Binding and Kinetics for Experimental Biologists Lecture 2 Evolutionary Computing: Initial Estimate Problem Petr Kuzmič, Ph.D. BioKin, Ltd. WATERTOWN,

More information

Bacterial Genetics & Operons

Bacterial Genetics & Operons Bacterial Genetics & Operons The Bacterial Genome Because bacteria have simple genomes, they are used most often in molecular genetics studies Most of what we know about bacterial genetics comes from the

More information

Evolutionary computation

Evolutionary computation Evolutionary computation Andrea Roli andrea.roli@unibo.it Dept. of Computer Science and Engineering (DISI) Campus of Cesena Alma Mater Studiorum Università di Bologna Outline 1 Basic principles 2 Genetic

More information

The phenotype of this worm is wild type. When both genes are mutant: The phenotype of this worm is double mutant Dpy and Unc phenotype.

The phenotype of this worm is wild type. When both genes are mutant: The phenotype of this worm is double mutant Dpy and Unc phenotype. Series 2: Cross Diagrams - Complementation There are two alleles for each trait in a diploid organism In C. elegans gene symbols are ALWAYS italicized. To represent two different genes on the same chromosome:

More information

Topic 7: Evolution. 1. The graph below represents the populations of two different species in an ecosystem over a period of several years.

Topic 7: Evolution. 1. The graph below represents the populations of two different species in an ecosystem over a period of several years. 1. The graph below represents the populations of two different species in an ecosystem over a period of several years. Which statement is a possible explanation for the changes shown? (1) Species A is

More information

EVOLUTIONARY COMPUTATION. Th. Back, Leiden Institute of Advanced Computer Science, Leiden University, NL and

EVOLUTIONARY COMPUTATION. Th. Back, Leiden Institute of Advanced Computer Science, Leiden University, NL and EVOLUTIONARY COMPUTATION Th. Back, Leiden Institute of Advanced Computer Science, Leiden University, NL and divis digital solutions GmbH, Dortmund, D. Keywords: adaptation, evolution strategy, evolutionary

More information

May 11, Aims: Agenda

May 11, Aims: Agenda May 11, 2017 Aims: SWBAT explain how survival of the fittest and natural selection have contributed to the continuation, extinction, and adaptation of species. Agenda 1. Do Now 2. Class Notes 3. Guided

More information

Parts 2. Modeling chromosome segregation

Parts 2. Modeling chromosome segregation Genome 371, Autumn 2017 Quiz Section 2 Meiosis Goals: To increase your familiarity with the molecular control of meiosis, outcomes of meiosis, and the important role of crossing over in generating genetic

More information

Lesson 4: Understanding Genetics

Lesson 4: Understanding Genetics Lesson 4: Understanding Genetics 1 Terms Alleles Chromosome Co dominance Crossover Deoxyribonucleic acid DNA Dominant Genetic code Genome Genotype Heredity Heritability Heritability estimate Heterozygous

More information

Reproduction- passing genetic information to the next generation

Reproduction- passing genetic information to the next generation 166 166 Essential Question: How has biological evolution led to the diversity of life? B-5 Natural Selection Traits that make an organism more or less likely to survive in an environment and reproduce

More information

4. Identify one bird that would most likely compete for food with the large tree finch. Support your answer. [1]

4. Identify one bird that would most likely compete for food with the large tree finch. Support your answer. [1] Name: Topic 5B 1. A hawk has a genetic trait that gives it much better eyesight than other hawks of the same species in the same area. Explain how this could lead to evolutionary change within this species

More information

Mutation, Selection, Gene Flow, Genetic Drift, and Nonrandom Mating Results in Evolution

Mutation, Selection, Gene Flow, Genetic Drift, and Nonrandom Mating Results in Evolution Mutation, Selection, Gene Flow, Genetic Drift, and Nonrandom Mating Results in Evolution 15.2 Intro In biology, evolution refers specifically to changes in the genetic makeup of populations over time.

More information

Complexity in social dynamics : from the. micro to the macro. Lecture 4. Franco Bagnoli. Lecture 4. Namur 7-18/4/2008

Complexity in social dynamics : from the. micro to the macro. Lecture 4. Franco Bagnoli. Lecture 4. Namur 7-18/4/2008 Complexity in Namur 7-18/4/2008 Outline 1 Evolutionary models. 2 Fitness landscapes. 3 Game theory. 4 Iterated games. Prisoner dilemma. 5 Finite populations. Evolutionary dynamics The word evolution is

More information

A Simple Haploid-Diploid Evolutionary Algorithm

A Simple Haploid-Diploid Evolutionary Algorithm A Simple Haploid-Diploid Evolutionary Algorithm Larry Bull Computer Science Research Centre University of the West of England, Bristol, UK larry.bull@uwe.ac.uk Abstract It has recently been suggested that

More information

Population Genetics I. Bio

Population Genetics I. Bio Population Genetics I. Bio5488-2018 Don Conrad dconrad@genetics.wustl.edu Why study population genetics? Functional Inference Demographic inference: History of mankind is written in our DNA. We can learn

More information

Evolution & Natural Selection

Evolution & Natural Selection Evolution & Natural Selection Learning Objectives Know what biological evolution is and understand the driving force behind biological evolution. know the major mechanisms that change allele frequencies

More information

1 Errors in mitosis and meiosis can result in chromosomal abnormalities.

1 Errors in mitosis and meiosis can result in chromosomal abnormalities. Slide 1 / 21 1 Errors in mitosis and meiosis can result in chromosomal abnormalities. a. Identify and describe a common chromosomal mutation. Slide 2 / 21 Errors in mitosis and meiosis can result in chromosomal

More information

Reading: Chapter 5, pp ; Reference chapter D, pp Problem set F

Reading: Chapter 5, pp ; Reference chapter D, pp Problem set F Mosaic Analysis Reading: Chapter 5, pp140-141; Reference chapter D, pp820-823 Problem set F Twin spots in Drosophila Although segregation and recombination in mitosis do not occur at the same frequency

More information

Microevolution Changing Allele Frequencies

Microevolution Changing Allele Frequencies Microevolution Changing Allele Frequencies Evolution Evolution is defined as a change in the inherited characteristics of biological populations over successive generations. Microevolution involves the

More information

Genetic Algorithm for Solving the Economic Load Dispatch

Genetic Algorithm for Solving the Economic Load Dispatch International Journal of Electronic and Electrical Engineering. ISSN 0974-2174, Volume 7, Number 5 (2014), pp. 523-528 International Research Publication House http://www.irphouse.com Genetic Algorithm

More information

Sex accelerates adaptation

Sex accelerates adaptation Molecular Evolution Sex accelerates adaptation A study confirms the classic theory that sex increases the rate of adaptive evolution by accelerating the speed at which beneficial mutations sweep through

More information

Binary Particle Swarm Optimization with Crossover Operation for Discrete Optimization

Binary Particle Swarm Optimization with Crossover Operation for Discrete Optimization Binary Particle Swarm Optimization with Crossover Operation for Discrete Optimization Deepak Singh Raipur Institute of Technology Raipur, India Vikas Singh ABV- Indian Institute of Information Technology

More information

Genetic Algorithms: Basic Principles and Applications

Genetic Algorithms: Basic Principles and Applications Genetic Algorithms: Basic Principles and Applications C. A. MURTHY MACHINE INTELLIGENCE UNIT INDIAN STATISTICAL INSTITUTE 203, B.T.ROAD KOLKATA-700108 e-mail: murthy@isical.ac.in Genetic algorithms (GAs)

More information

When to Use Bit-Wise Neutrality

When to Use Bit-Wise Neutrality When to Use Bit-Wise Neutrality Tobias Friedrich Department 1: Algorithms and Complexity Max-Planck-Institut für Informatik Saarbrücken, Germany Frank Neumann Department 1: Algorithms and Complexity Max-Planck-Institut

More information

chatper 17 Multiple Choice Identify the choice that best completes the statement or answers the question.

chatper 17 Multiple Choice Identify the choice that best completes the statement or answers the question. chatper 17 Multiple Choice Identify the choice that best completes the statement or answers the question. 1. If a mutation introduces a new skin color in a lizard population, which factor might determine

More information