Lesson Introduction to Evolutionary Computation

Quick Look

Grade Level: 11 (10-12)

Time Required: 45 minutes

Lesson Dependency: None

Quick Look

Partial design Partial design process
Grade Level:
11 (10 – 12)
Time Required:
45 minutes
Discuss this lesson

TE Newsletter

Photo shows a thumb and forefinger holding a small antenna that looks like miniature antlers made from bent silver wire.
NASA engineers use evolutionary computation to design tiny satellite antennas. Through a "survival of the fittest" approach, the software examines millions of potential antenna designs before settling on one.
copyright
Copyright © NASA http://www.nasa.gov/centers/ames/news/releases/2004/antenna/computer.html

Summary

Students are introduced to the concepts of evolution by natural selection and digital evolution software. They learn about the field of evolutionary computation, which applies the principles of natural selection to solve engineering design problems. They learn the similarities and differences between natural selection and the engineering design process.

Engineering Connection

Engineers apply scientific principles to design and build devices, structures, materials and processes. Typically, engineers identify a problem, brainstorm possible solutions and then create and test prototypes. Creating computational models helps engineers predict how prototypes or design decisions will react given certain parameters. Evolutionary computation applies the principles of evolution by natural selection to identify the best potential solutions and often suggest possible solutions that engineers would never have considered.

Learning Objectives

After this lesson, students should be able to:

  • Compare and contrast digital evolution with biological evolution.
  • Explain how the principles of natural selection can be applied to solve engineering design problems.

Worksheets and Attachments

Visit [www.teachengineering.org/curriculum/print/mis_avida2_lesson] to print or download.

Pre-Req Knowledge

A basic understanding of natural selection and the engineering design process is helpful.

Introduction/Motivation

(Show students the 15-slide Introduction to Evolutionary Computation Presentation, a PowerPoint file. Then lead a class discussion, asking students the following questions.)

  • What do engineers do? (Engineers apply their understanding of scientific and mathematical principles to design and create devices, structures, materials and processes.)
  • How do engineers decide what to make and how to make it? (Typically, engineers first identify a problem, then brainstorm possible solutions, and then create and test prototypes of the best design.)
  • What is evolution? (The change in the genetic composition of a population from generation to generation.)
  • What is the process of natural selection? (A process in which organisms with certain inherited characteristics are more likely to survive and reproduce than are organisms with other characteristics; the main driving force of evolution.)
  • How might the process of evolution by natural selection be similar to the engineering design process? (The process of evolution by natural selection is similar to the engineering design process in that it "tests" random variations of organisms in an environment, just as engineers test and refine their designs. Both natural selection and the engineering design process produce solutions to problems, such as finding the optimal structure for a particular task.)
  • How might natural selection and the engineering design process be different? (An important distinction between natural selection and the engineering design process is that one begins with the end in mind, while the other does not. Human inventions are the results of specific, goal-oriented research. Natural selection, on the other hand, is not goal-oriented. Engineers have recently begun to apply the principles of natural selection to solve engineering design problems using evolutionary computation.)

(Next, show students the PowerPoint presentation to review the concepts of natural selection and introduce evolutionary computation with Avida-ED. Have students answer the questions at the beginning/end of the presentation as a written assignment or the basis for a class discussion. Learn more about the small NASA antenna at the website listed in the References section. Show students the attached Avida-ED Tutorial Video to familiarize them with the software; see source information in the Additional Multimedia Support section). Following the lesson challenge students to complete the associated activity Competing Evolved and Engineered Digital Organisms to produce organisms with the highest fitness values in a particular environment through use of the free Avida-ED digital evolution software.

Lesson Background and Concepts for Teachers

About Avida-ED

You may wish to read the "Testing Darwin" article by Carl Zimmer in Discover Magazine to become familiar with the Avida software and the biological questions for which it is being used to answer. (See the Internet location of the article in the References section.)

Avida-ED is a software program adapted from the Avida research software described in the Discover Magazine article. Both programs can be described as instances of evolution in a model environment. The evolution itself is real; the digital organisms are subject to the process of natural selection just as biological organisms are. For biologists, the main advantages of using digital organisms are that 1) the environment can be precisely controlled and manipulated, and 2) the digital organisms reproduce much faster than any biological organisms. Avida-ED was created so students can learn about evolution by watching it in action. This powerful tool also enables students to design and perform their own experiments to test hypotheses about evolution in much the same way that researchers use Avida in their labs. Other lessons and activities for teaching evolutionary concepts using Avida-ED are published at www.teachengineering.org (search "Avida-ED").

The Avida-ED software is freely downloadable from http://avida-ed.msu.edu/.

About Evolutionary Computation

Please refer to the information in the attached PowerPoint file for an overview of concepts. For an excellent overview of digital evolution and its application in the design of computing systems, read the Harnessing Digital Evolution paper listed in the References section. For more background information, watch a short (3 minute) and/or a longer (18 minutes) video on evolutionary computation and its applications; the Internet locations are listed in the Additional Multimedia Support section.

Lesson Closure

As a class, review the answers to the discussion questions, as provided in the Assessment section.

Vocabulary/Definitions

Avida-ED: An educational version of the digital evolution software, Avida.

digital evolution: An instance of evolution wherein self-replicating digital organisms are subject to random mutation that is acted on by natural selection.

digital organism: A small, self-replicating computer program.

evolution: The change in the genetic composition of a population from generation to generation.

natural selection: A process in which organisms with certain inherited characteristics are more likely to survive and reproduce than are organisms with other characteristics; the main driving force of evolution.

Assessment

Written Answers: Collect students' written answers to the discussion questions posed at the beginning and end of the PowerPoint presentation (also listed below, with suggested answers). Alternatively, use their written answers as the basis for a class discussion.

  • How is digital evolution similar to biological evolution? How is it different? (Suggested answer: Digital organisms in Avida have an instruction set similar to the genetic information that biological organisms have in their DNA. Like biological organisms, the instruction set codes for "traits" of an organism as well as replication instructions for reproduction. The genetic information can also be changed by random mutation. The process of natural selection increases the proportion of adaptive traits in a natural (in the wild) or artificial (computational) population. The main difference is that in digital evolution, a user determines the characteristics of the environment and may set particular parameters for the solutions.)
  • How can the principles of natural selection be applied to solve engineering design problems? (Suggested answer: Digital organisms represent possible solutions to a particular problem. The user starts with one digital organism that is subject to random mutations to create many potential solutions. The user sets the characteristics of the environment to model real-world conditions and then allows the process of natural selection to determine the best solutions.)

Lesson Extension Activities

Conduct this lesson in conjunction with these other TeachEngineering curricula: Evolution of Digital Organisms lesson and its associated activity, Studying Evolution with Digital Organisms

Additional Multimedia Support

Show students the attached seven-minute Avida-ED tutorial video to familiarize them with the software. The same video also available on YouTube at: https://youtu.be/Z4fLAzYaKwE.

For teacher background information, watch Hampshire College's summary of evolutionary computation YouTube video (3 minutes) at: https://youtu.be/u1XSoB7rmL0

For teacher background information about evolutionary computation as well as predictions for future applications, watch this TEDxDanubia 2011 "Tech Kangaroos: Evolution at Work" YouTube video (18 minutes) at: https://youtu.be/WJX_wAKhg8A

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References

Bluck, John. "NASA 'Evolutionary' Software Automatically Designs Antenna." Posted June 14, 2004. Release 04-55AR, NASA Ames Research Center, Moffett Field, CA. Accessed November 1, 2012. (Description and photos about the NASA satellite antenna designed with evolutionary software) http://www.nasa.gov/centers/ames/news/releases/2004/04_55AR.html

McKinley, Philip, Betty H.C. Cheng, Charles Ofria, David Knoester, Benjamin Beckmann and Heather Goldsby (Michigan State University). January 2008. "Harnessing Digital Evolution." IEEE Computer Society. 41 (2008) pp. 54-63. (In digital evolution, self-replicating computer programs—digital organisms—experience mutations and selective pressures, potentially producing computational systems that, like natural organisms, adapt to their environments and protect themselves from threats. Such organisms can help guide the design of computer software.) Accessed November 1, 2012. http://www.cse.msu.edu/~mckinley/digital-evolution.pdf

Zimmer, Carl. "Testing Darwin." Published online February 5, 2005. Discover Magazine (February 2005 issue). Kalmbach Publishing Co. (Digital organisms that breed thousands of times faster than common bacteria are beginning to shed light on some of the biggest unanswered questions of evolution.) Accessed November 1, 2012. http://discovermagazine.com/2005/feb/cover

Copyright

© 2013 by Regents of the University of Colorado; original © 2011 Michigan State University

Contributors

Wendy Johnson

Supporting Program

Bio-Inspired Technology and Systems (BITS) RET, College of Engineering, Michigan State University

Acknowledgements

The contents of this digital library curriculum were developed through the Bio-Inspired Technology and Systems (BITS) RET program under National Science Foundation RET grant no EEG 0908810. However, these contents do not necessarily represent the policies of the NSF and you should not assume endorsement by the federal government.

Last modified: December 14, 2019

Hands-on Activity Competing Evolved and Engineered Digital Organisms

Quick Look

Grade Level: 11 (10-12)

Time Required: 2 hours

(can be split into two sessions)

Expendable Cost/Group: US $0.00

Requires the use of (non-expendable) computers, one for every two or three students, each loaded with free software.

Group Size: 2

Activity Dependency: None

Quick Look

Partial design Partial design process
Grade Level:
11 (10 – 12)
Time Required:
2 hours

(can be split into two sessions)

Group Size:
2
Discuss this activity

TE Newsletter

A man holds and examines a small model of a sleek silver airplane.
Engineers use evolutionary computation to optimize the wing design of supersonic aircraft.
copyright
Copyright © NASA/Michelle M. Murphy http://www.nasa.gov/topics/aeronautics/features/supersonic.html

Summary

Students engineer and evolve digital organisms with the challenge to produce organisms with the highest fitness values in a particular environment. They do this through use of the free Avida-ED digital evolution software application. The resulting organisms compete against each other in the same environment and students learn the benefits of applying the principles of natural selection to solve engineering design problems.

Engineering Connection

Engineers apply their understanding of scientific and math principles to design and create devices, structures, materials and processes. Typically, engineers identify a problem, brainstorm possible solutions, and then create and test prototypes to find the best solution. Engineers often use computer modeling for this type of optimization. Evolutionary computation applies the principles of evolution by natural selection to identify the best potential solutions and often suggests possible solutions that engineers would not have ever considered.

Learning Objectives

After this activity, students should be able to:

  • Explain how the principles of natural selection can be applied to solve engineering design problems.
  • Compare and contrast the process of natural selection and the engineering design process.

Materials List

Each group needs:

For the teacher:

  • flash drive or email access to obtain groups' text files of highest fitness organisms
  • computer connected to a projector (for the competition, operated by the instructor)

Worksheets and Attachments

Visit [www.teachengineering.org/curriculum/print/mis_avida2_lesson] to print or download.

Pre-Req Knowledge

A basic understanding of natural selection and the engineering design process is helpful.

Introduction/Motivation

(Review the associated Introduction to Evolutionary Computation lesson with students. They should have already been introduced to the Avida-ED software. You may wish to show the tutorial video again or allow some time for students to familiarize themselves with the program if they have not used it before. Have students be prepared with paper and pencil for taking notes during the Introduction/Motivation section, as described in the Assessment section.)

Drawing shows a futuristic green supersonic jet flying fast and high above the clouds.
Figure 1. Concept design for a supersonic aircraft that drastically lowers the level of sonic booms. Evolutionary computation methods are used to optimize the wing design of supersonic aircraft.
copyright
Copyright © NASA/Lockheed Martin http://www.nasa.gov/multimedia/imagegallery/image_feature_2025.html

How do engineers determine the optimum designs for supersonic aircraft? (Listen to student answers.) Engineers apply their knowledge of scientific and mathematical principles and brainstorm all sorts of possible design solutions. They often use computer modeling to help determine the most effective designs. Then they create and test prototypes in a continuing cycle to optimize the design.

As an example, evolutionary computation methods are applied to optimize the wing design of supersonic aircraft. (Show students the Figure 1 image of a supersonic aircraft design.) This drawing shows the concept for a supersonic aircraft design that drastically lowers the level of sonic booms. This design is intended to reduce the sonic shockwave signature so it barely registers on buildings and people below, and reduce drag.

How is the optimum design of aircraft similar to an adaptation of a biological organism? (Listen to student ideas.) Each part of an aircraft is designed for a specific purpose. For example, the wings are perfectly shaped to provide lift and the shape of the nose aides the aircraft in slicing through air---just as plants and animals have specific structures and characteristics that suit their functions.

What are some examples of plant and animal adaptations that make them well suited to their environments? (Listen to student answers; possible examples: the long neck of the giraffe for eating from tall trees, long mouthparts of an insect for extracting nectar from flowers.)

How does natural selection result in animals that are perfectly suited to their environments? (Listen to student ideas) Every individual in a population is born with unique random variations in their genes. Some of these random variations confer an advantage that helps the individual survive and reproduce more often than other individuals. In this way, the advantageous genes get passed on more often to offspring, which, over many generations, increases the proportion of that trait in a population.

An important distinction to remember is that the process of natural selection is not goal-oriented, while human inventions are the result of specific, goal-directed research. How can the process of natural selection be applied by engineers to solve engineering design problems? (Listen to student ideas.) Even though the process of natural selection is not-goal-oriented, it results in the increased fitness of organisms in their particular environments because of the differential reproductive success of random variations. Digital organisms are represented by a "genome" of computer commands that each represent a potential solution to a given problem. Engineers determine the parameters of a computational environment and may set constraints on the possible solutions. Then they let the process of natural selection sort through thousands of random variations to determine the "best fit" organisms, which represent the best potential solutions to the problem.

Today you have the opportunity to use the Avida-ED software to engineer and evolve digital organisms. Your challenge: To develop an organism with the highest fitness value. We will hold a competition at the end to determine which organism is the fittest.

Procedure

Background

Additional information about the Avida digital life platform including detailed information about the virtual hardware and instruction sets of digital organisms is included in the attached Avida Digital Life Platform Teacher Information. While this level of explanation is not necessary in order for students to manipulate the command sequences of genomes as described in the student handout, computer science teachers may decide to share this information with their students.

Before the Activity

With the Students

  1. Divide the class into small groups, two to three students per computer.
  2. Pass out the student handout. You may wish to demonstrate how to export the command sequence of a digital organism to a text file, edit it, and import it back into Avida-ED.
  3. Set up the activity in one of these ways:
  • Have each group engineer an organism and evolve an organism.
  • Have half the groups evolve an organism and the other half engineer an organism.
  1. Have groups each save their organism of highest fitness and submit them to the instructor by emailing the text file or saving it on a flash drive.
  2. Have teams compose and turn in written answers to the three questions on the handout.
  3. Hold a competition: The instructor competes two to four organisms at the same time using a computer connected to a projector. Idea: Make a "bracket" and have many rounds to determine the winner.
  4. Lead a class discussion to explore students' answers to the three handout discussion questions. See that students make the connection between the computer-simulated organisms and how engineers use computer models to help them determine various iterations of possible solutions.

Vocabulary/Definitions

Avida-ED: An educational version of the digital evolution software, Avida.

digital evolution: An instance of evolution wherein self-replicating digital organisms are subject to random mutation that is acted on by natural selection.

digital organism: A small, self-replicating computer program.

evolution: The change in the genetic composition of a population from generation to generation.

natural selection: A process in which organisms with certain inherited characteristics are more likely to survive and reproduce than are organisms with other characteristics; the main driving force of evolution.

Assessment

Pre-Activity Assessment

Opening Questions: Have students record and turn in their answers to the questions posed and discussed in the Introduction/Motivation section.

  • How do engineers determine the optimum designs for supersonic aircraft?
  • How is the optimum design of aircraft similar to an adaptation of a biological organism?
  • Can you give some examples of plant and animal adaptations that make them well suited to their environments?
  • How does natural selection result in animals that are perfectly suited to their environments?
  • How can the process of natural selection be applied by engineers to solve engineering design problems?
  • Bonus Question: What is the big distinction between processes of natural selection and human invention?

Activity Embedded Assessment

Written Answers: Have students turn in their written answers to the three questions on the Survival of the Fittest Student Handout.

Post-Activity Assessment

Concluding Discussion: Lead a class discussion to explore students' answers to the handout discussion questions. Make sure students make the connection between the computer-simulated organisms and how engineers use computer models to help them determine various iterations of possible solutions.

  • Compare and contrast the process of natural selection and the engineering design process.
  • Did evolved or engineered organisms have an advantage in the class competition? How do you account for these results?
  • Explain how the principles of natural selection can be applied to solve engineering design problems.

Subscribe

Get the inside scoop on all things TeachEngineering such as new site features, curriculum updates, video releases, and more by signing up for our newsletter!
PS: We do not share personal information or emails with anyone.

Copyright

© 2013 by Regents of the University of Colorado; original © 2012 Michigan State University

Contributors

Wendy Johnson; Jeff Farell

Supporting Program

Bio-Inspired Technology and Systems (BITS) RET, College of Engineering, Michigan State University

Acknowledgements

The contents of this digital library curriculum were developed through the Bio-Inspired Technology and Systems (BITS) RET program under National Science Foundation RET grant no EEG 0908810. However, these contents do not necessarily represent the policies of the NSF and you should not assume endorsement by the federal government.

Last modified: December 14, 2019