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# An Introduction To Applied Evolutionary Meta Heuristics

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This presentation introduces some of the main themes in modern evolutionary algorithm research while emphasising their application to problems that exhibit real-world complexity.

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### An Introduction To Applied Evolutionary Meta Heuristics

1. 1. An Introduction to Applied Evolutionary Metaheuristics Jonny Anderson <ul><ul><li>Image: http://www.43things.com/people/progress/dogonwheels/251399 </li></ul></ul>A strange attractor
2. 2. An Introduction to Applied Evolutionary Metaheuristics <ul><ul><li>Define “metaheuristic” </li></ul></ul><ul><ul><li>Examine evolutionary algorithms </li></ul></ul><ul><ul><li>Explore problem constraints </li></ul></ul><ul><ul><li>Apply an evolutionary algorithm to a farm </li></ul></ul><ul><ul><li>Discuss the implications for farm sustainability </li></ul></ul>
3. 3. What is a metaheuristic?
4. 4. What is a metaheuristic? <ul><ul><li>A heuristic is a function that finds an approximate result, it is an algorithmic rule of thumb </li></ul></ul><ul><ul><li>A metaheuristic is a function that consists of heuristic sub-routines. It is a compound heuristic. </li></ul></ul><ul><ul><li>Metaheuristics are useful for hard problems that resist brute-force techniques </li></ul></ul><ul><ul><li>A metaheuristic algorithm can quickly return results that are good enough </li></ul></ul><ul><ul><li>An example of a metaheuristic is an evolutionary algorithm </li></ul></ul>
5. 5. What is a metaheuristic? <ul><ul><li>A heuristic is a function that finds an approximate result, it is an algorithmic rule of thumb </li></ul></ul><ul><ul><li>A metaheuristic is a function that consists of heuristic sub-routines. It is a compound heuristic. </li></ul></ul><ul><ul><li>Metaheuristics are useful for hard problems that resist brute-force techniques </li></ul></ul><ul><ul><li>A metaheuristic algorithm can quickly return results that are good enough </li></ul></ul><ul><ul><li>An example of a metaheuristic is an evolutionary algorithm </li></ul></ul>
6. 6. What is a metaheuristic? <ul><ul><li>A heuristic is a function that finds an approximate result, it is an algorithmic rule of thumb </li></ul></ul><ul><ul><li>A metaheuristic is a function that consists of heuristic sub-routines. It is a compound heuristic. </li></ul></ul><ul><ul><li>Metaheuristics are useful for hard problems that resist brute-force techniques </li></ul></ul><ul><ul><li>A metaheuristic algorithm can quickly return results that are good enough </li></ul></ul><ul><ul><li>An example of a metaheuristic is an evolutionary algorithm </li></ul></ul>
7. 7. What is a metaheuristic? <ul><ul><li>A heuristic is a function that finds an approximate result, it is an algorithmic rule of thumb </li></ul></ul><ul><ul><li>A metaheuristic is a function that consists of heuristic sub-routines. It is a compound heuristic. </li></ul></ul><ul><ul><li>Metaheuristics are useful for hard problems that resist brute-force techniques </li></ul></ul><ul><ul><li>A metaheuristic algorithm can quickly return results that are good enough </li></ul></ul><ul><ul><li>An example of a metaheuristic is an evolutionary algorithm </li></ul></ul>
8. 8. What is a metaheuristic? <ul><ul><li>A heuristic is a function that finds an approximate result, it is an algorithmic rule of thumb </li></ul></ul><ul><ul><li>A metaheuristic is a function that consists of heuristic sub-routines. It is a compound heuristic. </li></ul></ul><ul><ul><li>Metaheuristics are useful for hard problems that resist brute-force techniques </li></ul></ul><ul><ul><li>A metaheuristic algorithm can quickly return results that are good enough </li></ul></ul><ul><ul><li>An example of a metaheuristic is an evolutionary algorithm </li></ul></ul>
9. 9. What is an evolutionary algorithm?
10. 10. What is an evolutionary algorithm <ul><ul><li>Natural selection discovers solutions to the problem of being alive </li></ul></ul><ul><ul><li>If we can translate this evolutionary process into a computer algorithm </li></ul></ul><ul><ul><li>Then we would have a powerful heuristic tool for discovering solutions to highly complex problems </li></ul></ul><ul><ul><li>An evolutionary algorithm encodes the process of natural selection </li></ul></ul><ul><ul><li>So that solutions to complex problems may be evolved </li></ul></ul><ul><ul><li>Image: http://en.wikipedia.org/wiki/Image:Charles_Darwin_by_Julia_Margaret_Cameron_2.jpg </li></ul></ul>Charles Darwin
11. 11. What is an evolutionary algorithm <ul><ul><li>Natural selection discovers solutions to the problem of being alive </li></ul></ul><ul><ul><li>If we can translate this evolutionary process into a computer algorithm </li></ul></ul><ul><ul><li>Then we would have a powerful heuristic tool for discovering solutions to highly complex problems </li></ul></ul><ul><ul><li>An evolutionary algorithm encodes the process of natural selection </li></ul></ul><ul><ul><li>So that solutions to complex problems may be evolved </li></ul></ul><ul><ul><li>Image: http://en.wikipedia.org/wiki/Image:Charles_Darwin_by_Julia_Margaret_Cameron_2.jpg </li></ul></ul>Charles Darwin
12. 12. What is an evolutionary algorithm <ul><ul><li>Natural selection discovers solutions to the problem of being alive </li></ul></ul><ul><ul><li>If we can translate this evolutionary process into a computer algorithm </li></ul></ul><ul><ul><li>Then we would have a powerful heuristic tool for discovering solutions to highly complex problems </li></ul></ul><ul><ul><li>An evolutionary algorithm encodes the process of natural selection </li></ul></ul><ul><ul><li>So that solutions to complex problems may be evolved </li></ul></ul><ul><ul><li>Image: http://en.wikipedia.org/wiki/Image:Charles_Darwin_by_Julia_Margaret_Cameron_2.jpg </li></ul></ul>Charles Darwin
13. 13. What is an evolutionary algorithm <ul><ul><li>Natural selection discovers solutions to the problem of being alive </li></ul></ul><ul><ul><li>If we can translate this evolutionary process into a computer algorithm </li></ul></ul><ul><ul><li>Then we would have a powerful heuristic tool for discovering solutions to highly complex problems </li></ul></ul><ul><ul><li>An evolutionary algorithm encodes the process of natural selection </li></ul></ul><ul><ul><li>So that solutions to complex problems may be evolved </li></ul></ul><ul><ul><li>Image: http://en.wikipedia.org/wiki/Image:Charles_Darwin_by_Julia_Margaret_Cameron_2.jpg </li></ul></ul>Charles Darwin
14. 14. What is an evolutionary algorithm <ul><ul><li>Natural selection discovers solutions to the problem of being alive </li></ul></ul><ul><ul><li>If we can translate this evolutionary process into a computer algorithm </li></ul></ul><ul><ul><li>Then we would have a powerful heuristic tool for discovering solutions to highly complex problems </li></ul></ul><ul><ul><li>An evolutionary algorithm encodes the process of natural selection </li></ul></ul><ul><ul><li>So that solutions to complex problems may be evolved </li></ul></ul><ul><ul><li>Image: http://en.wikipedia.org/wiki/Image:Charles_Darwin_by_Julia_Margaret_Cameron_2.jpg </li></ul></ul>Charles Darwin
15. 15. A general evolutionary algorithm
16. 16. A general evolutionary algorithm <ul><ul><li>Initialization Create a population of solutions with randomly generated characteristics </li></ul></ul><ul><ul><ul><li>Fitness assignment Assign a fitness value to each solution according to the problem-specific definition of fitness </li></ul></ul></ul><ul><ul><ul><li>Termination If stopping condition is met then Stop </li></ul></ul></ul><ul><ul><ul><li>Breeding and Variation Select the fittest solutions and c ombine them to create novel children (with a small chance of random mutation) </li></ul></ul></ul><ul><ul><ul><li>Go to Fitness Assignment </li></ul></ul></ul><ul><ul><ul><li>The result is an increase in average population fitness over time </li></ul></ul></ul>
17. 17. A general evolutionary algorithm <ul><ul><li>Initialization Create a population of solutions with randomly generated characteristics </li></ul></ul><ul><ul><ul><li>Fitness assignment Assign a fitness value to each solution according to the problem-specific definition of fitness </li></ul></ul></ul><ul><ul><ul><li>Termination If stopping condition is met then Stop </li></ul></ul></ul><ul><ul><ul><li>Breeding and Variation Select the fittest solutions and c ombine them to create novel children (with a small chance of random mutation) </li></ul></ul></ul><ul><ul><ul><li>Go to Fitness Assignment </li></ul></ul></ul><ul><ul><ul><li>The result is an increase in average population fitness over time </li></ul></ul></ul>
18. 18. A general evolutionary algorithm <ul><ul><li>Initialization Create a population of solutions with randomly generated characteristics </li></ul></ul><ul><ul><ul><li>Fitness assignment Assign a fitness value to each solution according to the problem-specific definition of fitness </li></ul></ul></ul><ul><ul><ul><li>Termination If stopping condition is met then Stop </li></ul></ul></ul><ul><ul><ul><li>Breeding and Variation Select the fittest solutions and c ombine them to create novel children (with a small chance of random mutation) </li></ul></ul></ul><ul><ul><ul><li>Go to Fitness Assignment </li></ul></ul></ul><ul><ul><ul><li>The result is an increase in average population fitness over time </li></ul></ul></ul>
19. 19. A general evolutionary algorithm <ul><ul><li>Initialization Create a population of solutions with randomly generated characteristics </li></ul></ul><ul><ul><ul><li>Fitness assignment Assign a fitness value to each solution according to the problem-specific definition of fitness </li></ul></ul></ul><ul><ul><ul><li>Termination If stopping condition is met then Stop </li></ul></ul></ul><ul><ul><ul><li>Breeding and Variation Select the fittest solutions and c ombine them to create novel children (with a small chance of random mutation) </li></ul></ul></ul><ul><ul><ul><li>Go to Fitness Assignment </li></ul></ul></ul><ul><ul><ul><li>The result is an increase in average population fitness over time </li></ul></ul></ul>
20. 20. A general evolutionary algorithm <ul><ul><li>Initialization Create a population of solutions with randomly generated characteristics </li></ul></ul><ul><ul><ul><li>Fitness assignment Assign a fitness value to each solution according to the problem-specific definition of fitness </li></ul></ul></ul><ul><ul><ul><li>Termination If stopping condition is met then Stop </li></ul></ul></ul><ul><ul><ul><li>Breeding and Variation Select the fittest solutions and c ombine them to create novel children (with a small chance of random mutation) </li></ul></ul></ul><ul><ul><ul><li>Go to Fitness Assignment </li></ul></ul></ul><ul><ul><ul><li>The result is an increase in average population fitness over time </li></ul></ul></ul>
21. 21. A general evolutionary algorithm <ul><ul><li>Initialization Create a population of solutions with randomly generated characteristics </li></ul></ul><ul><ul><ul><li>Fitness assignment Assign a fitness value to each solution according to the problem-specific definition of fitness </li></ul></ul></ul><ul><ul><ul><li>Termination If stopping condition is met then Stop </li></ul></ul></ul><ul><ul><ul><li>Breeding and Variation Select the fittest solutions and c ombine them to create novel children (with a small chance of random mutation) </li></ul></ul></ul><ul><ul><ul><li>Go to Fitness Assignment </li></ul></ul></ul><ul><ul><ul><li>The result is an increase in average population fitness over time </li></ul></ul></ul>
22. 22. Searching state-space
23. 23. Searching state-space <ul><ul><li>State-space is the set of all possible solution states </li></ul></ul><ul><ul><li>Certain problems have a large state-space making them difficult to solve </li></ul></ul><ul><ul><li>Evolutionary algorithms will evaluate many points in the state-space at the same time </li></ul></ul><ul><ul><li>This is called Implicit Parallelism </li></ul></ul><ul><ul><li>It allows evolutionary algorithms to remain effective for problems with a large state-space </li></ul></ul>4-D state-space Image: https://wci.llnl.gov/codes/visit/gallery_22.html
24. 24. Searching state-space <ul><ul><li>State-space is the set of all possible solution states </li></ul></ul><ul><ul><li>Certain problems have a large state-space making them difficult to solve </li></ul></ul><ul><ul><li>Evolutionary algorithms will evaluate many points in the state-space at the same time </li></ul></ul><ul><ul><li>This is called Implicit Parallelism </li></ul></ul><ul><ul><li>It allows evolutionary algorithms to remain effective for problems with a large state-space </li></ul></ul>4-D state-space Image: https://wci.llnl.gov/codes/visit/gallery_22.html
25. 25. Searching state-space <ul><ul><li>State-space is the set of all possible solution states </li></ul></ul><ul><ul><li>Certain problems have a large state-space making them difficult to solve </li></ul></ul><ul><ul><li>Evolutionary algorithms will evaluate many points in the state-space at the same time </li></ul></ul><ul><ul><li>This is called Implicit Parallelism </li></ul></ul><ul><ul><li>It allows evolutionary algorithms to remain effective for problems with a large state-space </li></ul></ul>4-D state-space Image: https://wci.llnl.gov/codes/visit/gallery_22.html
26. 26. Searching state-space <ul><ul><li>State-space is the set of all possible solution states </li></ul></ul><ul><ul><li>Certain problems have a large state-space making them difficult to solve </li></ul></ul><ul><ul><li>Evolutionary algorithms will evaluate many points in the state-space at the same time </li></ul></ul><ul><ul><li>This is called Implicit Parallelism </li></ul></ul><ul><ul><li>It allows evolutionary algorithms to remain effective for problems with a large state-space </li></ul></ul>4-D state-space Image: https://wci.llnl.gov/codes/visit/gallery_22.html
27. 27. Searching state-space <ul><ul><li>State-space is the set of all possible solution states </li></ul></ul><ul><ul><li>Certain problems have a large state-space making them difficult to solve </li></ul></ul><ul><ul><li>Evolutionary algorithms will evaluate many points in the state-space at the same time </li></ul></ul><ul><ul><li>This is called Implicit Parallelism </li></ul></ul><ul><ul><li>It allows evolutionary algorithms to remain effective for problems with a large state-space </li></ul></ul>4-D state-space Image: https://wci.llnl.gov/codes/visit/gallery_22.html
28. 28. Single objective evolutionary algorithms
29. 29. Single objective evolutionary algorithms <ul><ul><li>A single objective evolutionary algorithm exists to solve single objective problems </li></ul></ul><ul><ul><li>A standard single objective problem is the Travelling Salesperson Problem or TSP. This states: </li></ul></ul><ul><ul><li>Given a number of cities and the distance from any city to any other city. </li></ul></ul><ul><ul><li>What is the shortest round-trip route that visits each city exactly once and then returns to the starting city? </li></ul></ul><ul><ul><li>Objective = [minimise route length] </li></ul></ul><ul><ul><li>Image: http://www.cs.princeton.edu/courses/archive/spr05/cos126/assignments/tsp.html </li></ul></ul><ul><ul><li>TSP : 1000 Cities </li></ul></ul>
30. 30. Single objective evolutionary algorithms <ul><ul><li>A single objective evolutionary algorithm exists to solve single objective problems </li></ul></ul><ul><ul><li>A standard single objective problem is the Travelling Salesperson Problem or TSP. This states: </li></ul></ul><ul><ul><li>Given a number of cities and the distance from any city to any other city. </li></ul></ul><ul><ul><li>What is the shortest round-trip route that visits each city exactly once and then returns to the starting city? </li></ul></ul><ul><ul><li>Objective = [minimise route length] </li></ul></ul><ul><ul><li>Image: http://www.cs.princeton.edu/courses/archive/spr05/cos126/assignments/tsp.html </li></ul></ul><ul><ul><li>TSP : 1000 Cities </li></ul></ul>
31. 31. Single objective evolutionary algorithms <ul><ul><li>A single objective evolutionary algorithm exists to solve single objective problems </li></ul></ul><ul><ul><li>A standard single objective problem is the Travelling Salesperson Problem or TSP. This states: </li></ul></ul><ul><ul><li>Given a number of cities and the distance from any city to any other city. </li></ul></ul><ul><ul><li>What is the shortest round-trip route that visits each city exactly once and then returns to the starting city? </li></ul></ul><ul><ul><li>Objective = [minimise route length] </li></ul></ul><ul><ul><li>Image: http://www.cs.princeton.edu/courses/archive/spr05/cos126/assignments/tsp.html </li></ul></ul><ul><ul><li>TSP : 1000 Cities </li></ul></ul>
32. 32. Single objective evolutionary algorithms <ul><ul><li>A single objective evolutionary algorithm exists to solve single objective problems </li></ul></ul><ul><ul><li>A standard single objective problem is the Travelling Salesperson Problem or TSP. This states: </li></ul></ul><ul><ul><li>Given a number of cities and the distance from any city to any other city. </li></ul></ul><ul><ul><li>What is the shortest round-trip route that visits each city exactly once and then returns to the starting city? </li></ul></ul><ul><ul><li>Objective = [minimise route length] </li></ul></ul><ul><ul><li>Image: http://www.cs.princeton.edu/courses/archive/spr05/cos126/assignments/tsp.html </li></ul></ul><ul><ul><li>TSP : 1000 Cities </li></ul></ul>
33. 33. Single objective evolutionary algorithms <ul><ul><li>A single objective evolutionary algorithm exists to solve single objective problems </li></ul></ul><ul><ul><li>A standard single objective problem is the Travelling Salesperson Problem or TSP. This states: </li></ul></ul><ul><ul><li>Given a number of cities and the distance from any city to any other city. </li></ul></ul><ul><ul><li>What is the shortest round-trip route that visits each city exactly once and then returns to the starting city? </li></ul></ul><ul><ul><li>Objective = [ minimise route length ] </li></ul></ul><ul><ul><li>Image: http://www.cs.princeton.edu/courses/archive/spr05/cos126/assignments/tsp.html </li></ul></ul><ul><ul><li>TSP : 1000 Cities </li></ul></ul>
34. 34. Single objective evolutionary algorithms <ul><ul><li>Exact algorithms will give the shortest possible route but only work for 200 cities </li></ul></ul><ul><ul><li>Although evolutionary algorithms only promise a usefully short route </li></ul></ul><ul><ul><li>They work for 10,000 cities and beyond </li></ul></ul><ul><ul><li>Implicit parallelism allows evolutionary algorithms to search vast state-spaces </li></ul></ul>Image: https://wci.llnl.gov/codes/visit/gallery_22.html TSP : 100 cities
35. 35. Single objective evolutionary algorithms <ul><ul><li>Exact algorithms will give the shortest possible route but only work for 200 cities </li></ul></ul><ul><ul><li>Although evolutionary algorithms only promise a usefully short route </li></ul></ul><ul><ul><li>They work for 10,000 cities and beyond </li></ul></ul><ul><ul><li>Implicit parallelism allows evolutionary algorithms to search vast state-spaces </li></ul></ul>Image: https://wci.llnl.gov/codes/visit/gallery_22.html TSP : 100 cities
36. 36. Single objective evolutionary algorithms <ul><ul><li>Exact algorithms will give the shortest possible route but only work for 200 cities </li></ul></ul><ul><ul><li>Although evolutionary algorithms only promise a usefully short route </li></ul></ul><ul><ul><li>They work for 10,000 cities and beyond </li></ul></ul><ul><ul><li>Implicit parallelism allows evolutionary algorithms to search vast state-spaces </li></ul></ul>Image: https://wci.llnl.gov/codes/visit/gallery_22.html TSP : 100 cities TSP : 13509 cities
37. 37. Single objective evolutionary algorithms <ul><ul><li>Exact algorithms will give the shortest possible route but only work for 200 cities </li></ul></ul><ul><ul><li>Although evolutionary algorithms only promise a usefully short route </li></ul></ul><ul><ul><li>They work for 10,000 cities and beyond </li></ul></ul><ul><ul><li>Implicit parallelism allows evolutionary algorithms to search vast state-spaces </li></ul></ul>Image: https://wci.llnl.gov/codes/visit/gallery_22.html TSP : 100 cities TSP : 13509 cities
38. 38. Multi-objective evolutionary algorithms
39. 39. Multi-objective evolutionary algorithms <ul><ul><li>A multi-objective evolutionary algorithm exists to solve multi-objective problems </li></ul></ul><ul><ul><li>A multi-objective problem has two or more conflicting objectives - Maximising strength and minimising weight </li></ul></ul><ul><ul><li>This results in many solutions that balance the objectives in different ways </li></ul></ul><ul><ul><li>Such trade-off solutions are called Pareto optimal </li></ul></ul>Strength v Weight <ul><ul><li>Image : http://www.emergentarchitecture.com/analogies_images/analogy_18/medium.jpg </li></ul></ul>
40. 40. Multi-objective evolutionary algorithms <ul><ul><li>A multi-objective evolutionary algorithm exists to solve multi-objective problems </li></ul></ul><ul><ul><li>A multi-objective problem has two or more conflicting objectives - Maximising strength and minimising weight </li></ul></ul><ul><ul><li>This results in many solutions that balance the objectives in different ways </li></ul></ul><ul><ul><li>Such trade-off solutions are called Pareto optimal </li></ul></ul>Strength v Weight <ul><ul><li>Image : http://www.emergentarchitecture.com/analogies_images/analogy_18/medium.jpg </li></ul></ul>
41. 41. Multi-objective evolutionary algorithms <ul><ul><li>A multi-objective evolutionary algorithm exists to solve multi-objective problems </li></ul></ul><ul><ul><li>A multi-objective problem has two or more conflicting objectives - Maximising strength and minimising weight </li></ul></ul><ul><ul><li>This results in many solutions that balance the objectives in different ways </li></ul></ul><ul><ul><li>Such trade-off solutions are called Pareto optimal </li></ul></ul>Strength v Weight <ul><ul><li>Image : http://www.emergentarchitecture.com/analogies_images/analogy_18/medium.jpg </li></ul></ul>
42. 42. Multi-objective evolutionary algorithms <ul><ul><li>A multi-objective evolutionary algorithm exists to solve multi-objective problems </li></ul></ul><ul><ul><li>A multi-objective problem has two or more conflicting objectives - Maximising strength and minimising weight </li></ul></ul><ul><ul><li>This results in many solutions that balance the objectives in different ways </li></ul></ul><ul><ul><li>Such trade-off solutions are called Pareto optimal </li></ul></ul>Strength v Weight <ul><ul><li>Image : http://www.emergentarchitecture.com/analogies_images/analogy_18/medium.jpg </li></ul></ul>
43. 43. What is pareto optimality? [pa-rih-toe]
44. 44. What is pareto optimality? <ul><ul><li>A solution is pareto-optimal if it has been fully optimised </li></ul></ul><ul><ul><li>Optimising any objective will simply degrade the other objectives </li></ul></ul><ul><ul><li>While pareto-optimal solutions are equally optimal they are not equally useful </li></ul></ul><ul><ul><li>Therefore a human decision maker must make the final choice </li></ul></ul><ul><li>Image: http://www.longtail.typepad.com </li></ul><ul><ul><li>Image: Multi-objective Evolutionary Algorithms - A practical presentation of MOEA with examples from mechanical engineering Dr. Jörn Mehnen </li></ul></ul>Vilfredo Pareto
45. 45. What is pareto optimality? <ul><ul><li>A solution is pareto-optimal if it has been fully optimised </li></ul></ul><ul><ul><li>Optimising any objective will simply degrade the other objectives </li></ul></ul><ul><ul><li>While pareto-optimal solutions are equally optimal they are not equally useful </li></ul></ul><ul><ul><li>Therefore a human decision maker must make the final choice </li></ul></ul><ul><li>Image: http://www.longtail.typepad.com </li></ul><ul><ul><li>Image: Multi-objective Evolutionary Algorithms - A practical presentation of MOEA with examples from mechanical engineering Dr. Jörn Mehnen </li></ul></ul>Vilfredo Pareto
46. 46. What is pareto optimality? <ul><ul><li>A solution is pareto-optimal if it has been fully optimised </li></ul></ul><ul><ul><li>Optimising any objective will simply degrade the other objectives </li></ul></ul><ul><ul><li>While pareto-optimal solutions are equally optimal they are not equally useful </li></ul></ul><ul><ul><li>Therefore a human decision maker must make the final choice </li></ul></ul><ul><li>Image: http://www.longtail.typepad.com </li></ul><ul><ul><li>Image: Multi-objective Evolutionary Algorithms - A practical presentation of MOEA with examples from mechanical engineering Dr. Jörn Mehnen </li></ul></ul>Vilfredo Pareto
47. 47. What is pareto optimality? <ul><ul><li>A solution is pareto-optimal if it has been fully optimised </li></ul></ul><ul><ul><li>Optimising any objective will simply degrade the other objectives </li></ul></ul><ul><ul><li>While pareto-optimal solutions are equally optimal they are not equally useful </li></ul></ul><ul><ul><li>Therefore a human decision maker must make the final choice </li></ul></ul><ul><li>Image: http://www.longtail.typepad.com </li></ul><ul><ul><li>Image: Multi-objective Evolutionary Algorithms - A practical presentation of MOEA with examples from mechanical engineering Dr. Jörn Mehnen </li></ul></ul>Vilfredo Pareto
48. 48. Demo #1 The Pareto Front
49. 49. Multi-objective evolutionary algorithms Elitism
50. 50. Multi-objective evolutionary algorithms <ul><ul><li>Elitism </li></ul></ul><ul><ul><li>Elitism is used to help guide the evolving pareto front </li></ul></ul><ul><ul><li>The best solutions are retained in a secondary population called the archive </li></ul></ul><ul><ul><li>Solutions now compete to enter the elite archive </li></ul></ul><ul><ul><li>Only elite members are used for breeding </li></ul></ul><ul><ul><li>The archive acts a memory preventing the loss of fit solutions </li></ul></ul>Image: http://www.irishrunner.com/jamesnwc.jpg Elite Competition
51. 51. Multi-objective evolutionary algorithms <ul><ul><li>Elitism </li></ul></ul><ul><ul><li>Elitism is used to help guide the evolving pareto front </li></ul></ul><ul><ul><li>The best solutions are retained in a secondary population called the archive </li></ul></ul><ul><ul><li>Solutions now compete to enter the elite archive </li></ul></ul><ul><ul><li>Only elite members are used for breeding </li></ul></ul><ul><ul><li>The archive acts a memory preventing the loss of fit solutions </li></ul></ul>Image: http://www.irishrunner.com/jamesnwc.jpg Elite Competition
52. 52. Multi-objective evolutionary algorithms <ul><ul><li>Elitism </li></ul></ul><ul><ul><li>Elitism is used to help guide the evolving pareto front </li></ul></ul><ul><ul><li>The best solutions are retained in a secondary population called the archive </li></ul></ul><ul><ul><li>Solutions now compete to enter the elite archive </li></ul></ul><ul><ul><li>Only elite members are used for breeding </li></ul></ul><ul><ul><li>The archive acts a memory preventing the loss of fit solutions </li></ul></ul>Image: http://www.irishrunner.com/jamesnwc.jpg Elite Competition
53. 53. Multi-objective evolutionary algorithms <ul><ul><li>Elitism </li></ul></ul><ul><ul><li>Elitism is used to help guide the evolving pareto front </li></ul></ul><ul><ul><li>The best solutions are retained in a secondary population called the archive </li></ul></ul><ul><ul><li>Solutions now compete to enter the elite archive </li></ul></ul><ul><ul><li>Only elite members are used for breeding </li></ul></ul><ul><ul><li>The archive acts a memory preventing the loss of fit solutions </li></ul></ul>Image: http://www.irishrunner.com/jamesnwc.jpg Elite Competition
54. 54. Multi-objective evolutionary algorithms <ul><ul><li>Elitism </li></ul></ul><ul><ul><li>Elitism is used to help guide the evolving pareto front </li></ul></ul><ul><ul><li>The best solutions are retained in a secondary population called the archive </li></ul></ul><ul><ul><li>Solutions now compete to enter the elite archive </li></ul></ul><ul><ul><li>Only elite members are used for breeding </li></ul></ul><ul><ul><li>The archive acts a memory preventing the loss of fit solutions </li></ul></ul>Image: http://www.irishrunner.com/jamesnwc.jpg Elite Competition
55. 55. Multi-Objective Evolutionary Algorithms Maximise the diversity
56. 56. Multi-objective evolutionary algorithms <ul><ul><li>Maximise the diversity </li></ul></ul><ul><ul><li>An evolving front tends to generate clusters and so lose diversity </li></ul></ul><ul><ul><li>Clusters waste resources by confining the search to a small areas </li></ul></ul><ul><ul><li>Leaving the gaps in between poorly investigated </li></ul></ul><ul><ul><li>Image adapted : Zitzler, Eckart and Laumanns, Marco and Thiele, Lothar (2001) SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Evolutionary Methods for Design, Optimisation, and Control. </li></ul></ul>
57. 57. Multi-objective evolutionary algorithms <ul><ul><li>Maximise the diversity </li></ul></ul><ul><ul><li>An evolving front tends to generate clusters and so lose diversity </li></ul></ul><ul><ul><li>Clusters waste resources by confining the search to a small areas </li></ul></ul><ul><ul><li>Leaving the gaps in between poorly investigated </li></ul></ul><ul><ul><li>Image adapted : Zitzler, Eckart and Laumanns, Marco and Thiele, Lothar (2001) SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Evolutionary Methods for Design, Optimisation, and Control. </li></ul></ul>
58. 58. Multi-objective evolutionary algorithms <ul><ul><li>Maximise the diversity </li></ul></ul><ul><ul><li>An evolving front tends to generate clusters and so lose diversity </li></ul></ul><ul><ul><li>Clusters waste resources by confining the search to a small areas </li></ul></ul><ul><ul><li>Leaving the gaps in between poorly investigated </li></ul></ul><ul><ul><li>Image adapted : Zitzler, Eckart and Laumanns, Marco and Thiele, Lothar (2001) SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Evolutionary Methods for Design, Optimisation, and Control. </li></ul></ul>
59. 59. Multi-objective evolutionary algorithms <ul><ul><li>Maximise the diversity </li></ul></ul><ul><ul><li>Diversity can be maintained through pruning </li></ul></ul><ul><ul><li>Solutions that lie too clos e to their neighbours are removed </li></ul></ul><ul><ul><li>The goal is to have each solution evenly spaced with respect to its neighbours </li></ul></ul><ul><ul><li>Image adapted : Zitzler, Eckart and Laumanns, Marco and Thiele, Lothar (2001) SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Evolutionary Methods for Design, Optimisation, and Control. </li></ul></ul>
60. 60. Multi-objective evolutionary algorithms <ul><ul><li>Maximise the diversity </li></ul></ul><ul><ul><li>Diversity can be maintained through pruning </li></ul></ul><ul><ul><li>Solutions that lie too clos e to their neighbours are removed </li></ul></ul><ul><ul><li>The goal is to have each solution evenly spaced with respect to its neighbours </li></ul></ul><ul><ul><li>Image adapted : Zitzler, Eckart and Laumanns, Marco and Thiele, Lothar (2001) SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Evolutionary Methods for Design, Optimisation, and Control. </li></ul></ul>
61. 61. Multi-objective evolutionary algorithms <ul><ul><li>Maximise the diversity </li></ul></ul><ul><ul><li>Diversity can be maintained through pruning </li></ul></ul><ul><ul><li>Solutions that lie too clos e to their neighbours are removed </li></ul></ul><ul><ul><li>The goal is to have each solution evenly spaced with respect to its neighbours </li></ul></ul><ul><ul><li>Image adapted : Zitzler, Eckart and Laumanns, Marco and Thiele, Lothar (2001) SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Evolutionary Methods for Design, Optimisation, and Control. </li></ul></ul>
62. 62. Demo #2 Diversity & Constraints
63. 63. What is a constraint?
64. 64. What is a Constraint? <ul><ul><li>Solution Feasibility </li></ul></ul><ul><ul><li>All real world problems are bounded by physical limits </li></ul></ul><ul><ul><li>Solutions that lie outside of these limits are infeasible </li></ul></ul><ul><ul><li>Constraints model the physical limits of a problem </li></ul></ul><ul><ul><li>If a solution violates one or more of its constraints then it is considered infeasible </li></ul></ul><ul><ul><li>Image adapted: http://www.britannica.com/EBchecked/topic-art/430575/3028/Constraint-set-bounded-by-the-five-lines-x1-0-x2 </li></ul></ul>
65. 65. What is a Constraint? <ul><ul><li>Solution Feasibility </li></ul></ul><ul><ul><li>All real world problems are bounded by physical limits </li></ul></ul><ul><ul><li>Solutions that lie outside of these limits are infeasible </li></ul></ul><ul><ul><li>Constraints model the physical limits of a problem </li></ul></ul><ul><ul><li>If a solution violates one or more of its constraints then it is considered infeasible </li></ul></ul><ul><ul><li>Image adapted: http://www.britannica.com/EBchecked/topic-art/430575/3028/Constraint-set-bounded-by-the-five-lines-x1-0-x2 </li></ul></ul>
66. 66. What is a Constraint? <ul><ul><li>Solution Feasibility </li></ul></ul><ul><ul><li>All real world problems are bounded by physical limits </li></ul></ul><ul><ul><li>Solutions that lie outside of these limits are infeasible </li></ul></ul><ul><ul><li>Constraints model the physical limits of a problem </li></ul></ul><ul><ul><li>If a solution violates one or more of its constraints then it is considered infeasible </li></ul></ul><ul><ul><li>Image adapted: http://www.britannica.com/EBchecked/topic-art/430575/3028/Constraint-set-bounded-by-the-five-lines-x1-0-x2 </li></ul></ul>
67. 67. What is a Constraint? <ul><ul><li>Solution Feasibility </li></ul></ul><ul><ul><li>All real world problems are bounded by physical limits </li></ul></ul><ul><ul><li>Solutions that lie outside of these limits are infeasible </li></ul></ul><ul><ul><li>Constraints model the physical limits of a problem </li></ul></ul><ul><ul><li>If a solution violates one or more of its constraints then it is considered infeasible </li></ul></ul><ul><ul><li>Image adapted: http://www.britannica.com/EBchecked/topic-art/430575/3028/Constraint-set-bounded-by-the-five-lines-x1-0-x2 </li></ul></ul>
68. 68. What is a Constraint? <ul><ul><li>The Problem with Constraints </li></ul></ul><ul><ul><li>It is hard to randomly search for an initial population of feasible solutions </li></ul></ul><ul><ul><li>Better to generate an infeasible population and evolve it towards feasibility </li></ul></ul><ul><ul><li>This is done by adding a new infeasibility objective to the multi-objective problem: Infeasibility Objective = [minimise population infeasibility] </li></ul></ul>Image: http://en.wikipedia.org/wiki/Image:Random_walk_in2D_closeup.png Image: http://www.outbacksoftware.com/mathematica/mathematica-intro.html Random walk - 3D Random walk - 2D
69. 69. What is a Constraint? <ul><ul><li>The Problem with Constraints </li></ul></ul><ul><ul><li>It is hard to randomly search for an initial population of feasible solutions </li></ul></ul><ul><ul><li>Better to generate an infeasible population and evolve it towards feasibility </li></ul></ul><ul><ul><li>This is done by adding a new infeasibility objective to the multi-objective problem: Infeasibility Objective = [minimise population infeasibility] </li></ul></ul>Image: http://en.wikipedia.org/wiki/Image:Random_walk_in2D_closeup.png Image: http://www.outbacksoftware.com/mathematica/mathematica-intro.html Random walk - 3D Random walk - 2D
70. 70. What is a Constraint? <ul><ul><li>The Problem with Constraints </li></ul></ul><ul><ul><li>It is hard to randomly search for an initial population of feasible solutions </li></ul></ul><ul><ul><li>Better to generate an infeasible population and evolve it towards feasibility </li></ul></ul><ul><ul><li>This is done by adding a new infeasibility objective to the multi-objective problem: Infeasibility Objective = [ minimise population infeasibility ] </li></ul></ul>Image: http://en.wikipedia.org/wiki/Image:Random_walk_in2D_closeup.png Image: http://www.outbacksoftware.com/mathematica/mathematica-intro.html Random walk - 3D Random walk - 2D
71. 71. Summary <ul><ul><li>Defined “metaheuristic” </li></ul></ul><ul><ul><li>Examined evolutionary algorithms </li></ul></ul><ul><ul><li>Explored problem constraints </li></ul></ul><ul><ul><li>Apply an evolutionary algorithm to a farm </li></ul></ul><ul><ul><li>Discuss the implications for farm sustainability </li></ul></ul>
72. 72. Applying an evolutionary algorithm IC-SPEA2
73. 73. Applying an evolutionary algorithm <ul><ul><li>IC-SPEA2 </li></ul></ul><ul><ul><li>IC-SPEA2 is a powerful multi-objective evolutionary metaheuristic </li></ul></ul><ul><ul><li>It uses an infeasibility objective to evolve feasible solutions </li></ul></ul><ul><ul><li>And elitism to guide the search </li></ul></ul><ul><ul><li>And pruning to maximise the diversity </li></ul></ul>
74. 74. Applying an evolutionary algorithm <ul><ul><li>IC-SPEA2 </li></ul></ul><ul><ul><li>IC-SPEA2 is a powerful multi-objective evolutionary metaheuristic </li></ul></ul><ul><ul><li>It uses an infeasibility objective to evolve feasible solutions </li></ul></ul><ul><ul><li>And elitism to guide the search </li></ul></ul><ul><ul><li>And pruning to maximise the diversity </li></ul></ul>
75. 75. Applying an evolutionary algorithm <ul><ul><li>IC-SPEA2 </li></ul></ul><ul><ul><li>IC-SPEA2 is a powerful multi-objective evolutionary metaheuristic </li></ul></ul><ul><ul><li>It uses an infeasibility objective to evolve feasible solutions </li></ul></ul><ul><ul><li>And elitism to guide the search </li></ul></ul><ul><ul><li>And pruning to maximise the diversity </li></ul></ul>
76. 76. Applying an evolutionary algorithm <ul><ul><li>IC-SPEA2 </li></ul></ul><ul><ul><li>IC-SPEA2 is a powerful multi-objective evolutionary metaheuristic </li></ul></ul><ul><ul><li>It uses an infeasibility objective to evolve feasible solutions </li></ul></ul><ul><ul><li>And elitism to guide the search </li></ul></ul><ul><ul><li>And pruning to maximise the diversity </li></ul></ul>
77. 77. Applying an evolutionary metaheuristic A beef farm model
78. 78. Applying an evolutionary metaheuristic <ul><ul><li>A beef farm model </li></ul></ul><ul><ul><li>A farm is a complex dynamic system </li></ul></ul><ul><ul><li>It is bounded by multiple trade-offs , constraints and variable measures of success </li></ul></ul><ul><ul><li>These characteristics can be encoded into a software model </li></ul></ul><ul><ul><li>And the model can be optimised using IC-SPEA2 </li></ul></ul>
79. 79. Applying an evolutionary metaheuristic <ul><ul><li>A beef farm model </li></ul></ul><ul><ul><li>A farm is a complex dynamic system </li></ul></ul><ul><ul><li>It is bounded by multiple trade-offs , constraints and variable measures of success </li></ul></ul><ul><ul><li>These characteristics can be encoded into a software model </li></ul></ul><ul><ul><li>And the model can be optimised using IC-SPEA2 </li></ul></ul>
80. 80. Applying an evolutionary metaheuristic <ul><ul><li>A beef farm model </li></ul></ul><ul><ul><li>A farm is a complex dynamic system </li></ul></ul><ul><ul><li>It is bounded by multiple trade-offs , constraints and variable measures of success </li></ul></ul><ul><ul><li>These characteristics can be encoded into a software model </li></ul></ul><ul><ul><li>And the model can be optimised using IC-SPEA2 </li></ul></ul>
81. 81. Applying an evolutionary metaheuristic <ul><ul><li>A beef farm model </li></ul></ul><ul><ul><li>A farm is a complex dynamic system </li></ul></ul><ul><ul><li>It is bounded by multiple trade-offs , constraints and variable measures of success </li></ul></ul><ul><ul><li>These characteristics can be encoded into a software model </li></ul></ul><ul><ul><li>And the model can be optimised using IC-SPEA2 </li></ul></ul>
82. 82. Applying an evolutionary metaheuristic The farm loop
83. 83. Applying an evolutionary metaheuristic <ul><ul><li>The farm loop </li></ul></ul><ul><li>1 .The farmer selects and implements a strategy </li></ul><ul><li>2 .The farm provides real-world values for the model </li></ul><ul><li>3 .The model provides a template for IC-SPEA2 </li></ul><ul><li>4 .IC-SPEA2 evolves a population of model instances </li></ul><ul><li>5 .IC-SPEA2 presents a Pareto set of optimal models. </li></ul><ul><li>Return to Step 1 </li></ul>
84. 84. Applying an evolutionary metaheuristic <ul><ul><li>The farm loop </li></ul></ul><ul><li>1 .The farmer selects and implements a strategy </li></ul><ul><li>2 .The farm provides real-world values for the model </li></ul><ul><li>3 .The model provides a template for IC-SPEA2 </li></ul><ul><li>4 .IC-SPEA2 evolves a population of model instances </li></ul><ul><li>5 .IC-SPEA2 presents a Pareto set of optimal models. </li></ul><ul><li>Return to Step 1 </li></ul>
85. 85. Applying an evolutionary metaheuristic <ul><ul><li>The farm loop </li></ul></ul><ul><li>1 .The farmer selects and implements a strategy </li></ul><ul><li>2 .The farm provides real-world values for the model </li></ul><ul><li>3 .The model provides a template for IC-SPEA2 </li></ul><ul><li>4 .IC-SPEA2 evolves a population of model instances </li></ul><ul><li>5 .IC-SPEA2 presents a Pareto set of optimal models. </li></ul><ul><li>Return to Step 1 </li></ul>
86. 86. Applying an evolutionary metaheuristic <ul><ul><li>The farm loop </li></ul></ul><ul><li>1 .The farmer selects and implements a strategy </li></ul><ul><li>2 .The farm provides real-world values for the model </li></ul><ul><li>3 .The model provides a template for IC-SPEA2 </li></ul><ul><li>4 .IC-SPEA2 evolves a population of model instances </li></ul><ul><li>5 .IC-SPEA2 presents a Pareto set of optimal models. </li></ul><ul><li>Return to Step 1 </li></ul>
87. 87. Applying an evolutionary metaheuristic <ul><ul><li>The farm loop </li></ul></ul><ul><li>1 .The farmer selects and implements a strategy </li></ul><ul><li>2 .The farm provides real-world values for the model </li></ul><ul><li>3 .The model provides a template for IC-SPEA2 </li></ul><ul><li>4 .IC-SPEA2 evolves a population of model instances </li></ul><ul><li>5 .IC-SPEA2 presents a Pareto set of optimal models. </li></ul><ul><li>Return to Step 1 </li></ul>
88. 88. Applying an evolutionary metaheuristic The farm's objectives
89. 89. Applying an evolutionary metaheuristic <ul><ul><li>The farms' objectives </li></ul></ul><ul><ul><li>The farm makes money by converting food into live animal weight </li></ul></ul><ul><ul><li>Different food types have different nutritional benefits and different and purchase / production costs </li></ul></ul><ul><ul><li>IC-SPEA2 will search for combinations of feed type and amounts to create optimal feeding schedules </li></ul></ul>Live Weight Gain
90. 90. Applying an evolutionary metaheuristic <ul><ul><li>The farms' objectives </li></ul></ul><ul><ul><li>The farm makes money by converting food into live animal weight </li></ul></ul><ul><ul><li>Different food types have different nutritional benefits and different and purchase / production costs </li></ul></ul><ul><ul><li>IC-SPEA2 will search for combinations of feed type and amounts to create optimal feeding schedules </li></ul></ul>Live Weight Gain
91. 91. Applying an evolutionary metaheuristic <ul><ul><li>The farms' objectives </li></ul></ul><ul><ul><li>The farm makes money by converting food into live animal weight </li></ul></ul><ul><ul><li>Different food types have different nutritional benefits and different and purchase / production costs </li></ul></ul><ul><ul><li>IC-SPEA2 will search for combinations of feed type and amounts to create optimal feeding schedules </li></ul></ul>Live Weight Gain
92. 92. Applying an evolutionary metaheuristic <ul><ul><li>The farms' objectives </li></ul></ul><ul><ul><li>The conflicting objectives for the farm model are </li></ul></ul><ul><ul><li>1. Maximise net revenue By creating the heaviest animals using the cheapest feed </li></ul></ul><ul><ul><li>2. Maximise average daily weight gain This conflicts with “maximise net revenue” since heavier animals cost more to keep </li></ul></ul><ul><ul><li>3. Minimise the cost of the diet This conflicts with “maximise average daily weight gain” since cheap food have low nutritional value </li></ul></ul>
93. 93. Applying an evolutionary metaheuristic <ul><ul><li>The farms' objectives </li></ul></ul><ul><ul><li>The conflicting objectives for the farm model are </li></ul></ul><ul><ul><li>1. Maximise net revenue By creating the heaviest animals using the cheapest feed </li></ul></ul><ul><ul><li>2. Maximise average daily weight gain This conflicts with “maximise net revenue” since heavier animals cost more to keep </li></ul></ul><ul><ul><li>3. Minimise the cost of the diet This conflicts with “maximise average daily weight gain” since cheap food have low nutritional value </li></ul></ul>
94. 94. Applying an evolutionary metaheuristic <ul><ul><li>The farms' objectives </li></ul></ul><ul><ul><li>The conflicting objectives for the farm model are </li></ul></ul><ul><ul><li>1. Maximise net revenue By creating the heaviest animals using the cheapest feed </li></ul></ul><ul><ul><li>2. Maximise average daily weight gain This conflicts with “maximise net revenue” since heavier animals cost more to keep </li></ul></ul><ul><ul><li>3. Minimise the cost of the diet This conflicts with “maximise average daily weight gain” since cheap food have low nutritional value </li></ul></ul>
95. 95. Applying an evolutionary metaheuristic <ul><ul><li>The farms' objectives </li></ul></ul><ul><ul><li>The conflicting objectives for the farm model are </li></ul></ul><ul><ul><li>1. Maximise net revenue By creating the heaviest animals using the cheapest feed </li></ul></ul><ul><ul><li>2. Maximise average daily weight gain This conflicts with “maximise net revenue” since heavier animals cost more to keep </li></ul></ul><ul><ul><li>3. Minimise the cost of the diet This conflicts with “maximise average daily weight gain” since cheap food have low nutritional value </li></ul></ul>
96. 96. Applying an evolutionary metaheuristic Discovering counter intuitive solutions
97. 97. Applying an evolutionary metaheuristic <ul><ul><li>Discovering counter intuitive solutions </li></ul></ul><ul><ul><li>As expected IC-SPEA2 returned multiple, pareto-optimal strategies </li></ul></ul><ul><ul><li>Two solutions were of particular interest 1. The feeding strategy that maximised net revenue. 2. The feeding strategy that maximised live weight gain . </li></ul></ul><ul><ul><li>Intuition suggests that heavy animals should sell for more money [maximum live weight gain] = [maximum net revenue] </li></ul></ul><ul><ul><li>Yet IC-SPEA2 found a way to maximise net revenue while delivering lighter animals </li></ul></ul><ul><ul><li>This counter-intuitive result has potential implications for farm sustainability </li></ul></ul>
98. 98. Applying an evolutionary metaheuristic <ul><ul><li>Discovering counter intuitive solutions </li></ul></ul><ul><ul><li>As expected IC-SPEA2 returned multiple, pareto-optimal strategies </li></ul></ul><ul><ul><li>Two solutions were of particular interest 1. The feeding strategy that maximised net revenue. 2. The feeding strategy that maximised live weight gain . </li></ul></ul><ul><ul><li>Intuition suggests that heavy animals should sell for more money [maximum live weight gain] = [maximum net revenue] </li></ul></ul><ul><ul><li>Yet IC-SPEA2 found a way to maximise net revenue while delivering lighter animals </li></ul></ul><ul><ul><li>This counter-intuitive result has potential implications for farm sustainability </li></ul></ul>
99. 99. Applying an evolutionary metaheuristic <ul><ul><li>Discovering counter intuitive solutions </li></ul></ul><ul><ul><li>As expected IC-SPEA2 returned multiple, pareto-optimal strategies </li></ul></ul><ul><ul><li>Two solutions were of particular interest 1. The feeding strategy that maximised net revenue. 2. The feeding strategy that maximised live weight gain . </li></ul></ul><ul><ul><li>Intuition suggests that heavy animals should sell for more money [maximum live weight gain] = [maximum net revenue] </li></ul></ul><ul><ul><li>Yet IC-SPEA2 found a way to maximise net revenue while delivering lighter animals </li></ul></ul><ul><ul><li>This counter-intuitive result has potential implications for farm sustainability </li></ul></ul>
100. 100. Applying an evolutionary metaheuristic <ul><ul><li>Discovering counter intuitive solutions </li></ul></ul><ul><ul><li>As expected IC-SPEA2 returned multiple, pareto-optimal strategies </li></ul></ul><ul><ul><li>Two solutions were of particular interest 1. The feeding strategy that maximised net revenue. 2. The feeding strategy that maximised live weight gain . </li></ul></ul><ul><ul><li>Intuition suggests that heavy animals should sell for more money [ maximum live weight gain ] = [ maximum net revenue ] </li></ul></ul><ul><ul><li>Yet IC-SPEA2 found a way to maximise net revenue while delivering lighter animals </li></ul></ul><ul><ul><li>This counter-intuitive result has potential implications for farm sustainability </li></ul></ul>
101. 101. Applying an evolutionary metaheuristic <ul><ul><li>Discovering counter intuitive solutions </li></ul></ul><ul><ul><li>As expected IC-SPEA2 returned multiple, pareto-optimal strategies </li></ul></ul><ul><ul><li>Two solutions were of particular interest 1. The feeding strategy that maximised net revenue. 2. The feeding strategy that maximised live weight gain . </li></ul></ul><ul><ul><li>Intuition suggests that heavy animals should sell for more money [maximum live weight gain] = [maximum net revenue] </li></ul></ul><ul><ul><li>Yet IC-SPEA2 found a way to maximise net revenue while delivering lighter animals </li></ul></ul><ul><ul><li>This counter-intuitive result has potential implications for farm sustainability </li></ul></ul>
102. 102. Applying an evolutionary metaheuristic <ul><ul><li>Discovering counter intuitive solutions </li></ul></ul><ul><ul><li>As expected IC-SPEA2 returned multiple, pareto-optimal strategies </li></ul></ul><ul><ul><li>Two solutions were of particular interest 1. The feeding strategy that maximised net revenue. 2. The feeding strategy that maximised live weight gain . </li></ul></ul><ul><ul><li>Intuition suggests that heavy animals should sell for more money [maximum live weight gain] = [maximum net revenue] </li></ul></ul><ul><ul><li>Yet IC-SPEA2 found a way to maximise net revenue while delivering lighter animals </li></ul></ul><ul><ul><li>This counter-intuitive result has potential implications for farm sustainability </li></ul></ul>
103. 103. Applying an evolutionary metaheuristic Implications for farm sustainability
104. 104. Applying an evolutionary metaheuristic <ul><ul><li>Implications for farm sustainability </li></ul></ul><ul><ul><li>1. Reduce stress </li></ul></ul><ul><ul><li>Maintaining net revenue while reducing live weight gain will reduce the stress on the farm system </li></ul></ul><ul><ul><li>Profit obligations are met even as the farm operates below its carrying capacity </li></ul></ul><ul><ul><li>2. Prevent over-shoot </li></ul></ul><ul><ul><li>Real-time software allows frequent strategic re-evaluations </li></ul></ul><ul><ul><li>Frequent adjustments may help buffer against performance over-shoot </li></ul></ul>
105. 105. Applying an evolutionary metaheuristic <ul><ul><li>Implications for farm sustainability </li></ul></ul><ul><ul><li>1. Reduce stress </li></ul></ul><ul><ul><li>Maintaining net revenue while reducing live weight gain will reduce the stress on the farm system </li></ul></ul><ul><ul><li>Profit obligations are met even as the farm operates below its carrying capacity </li></ul></ul><ul><ul><li>2. Prevent over-shoot </li></ul></ul><ul><ul><li>Real-time software allows frequent strategic re-evaluations </li></ul></ul><ul><ul><li>Frequent adjustments may help buffer against performance over-shoot </li></ul></ul>
106. 106. Applying an evolutionary metaheuristic <ul><ul><li>Implications for farm sustainability </li></ul></ul><ul><ul><li>1. Reduce stress </li></ul></ul><ul><ul><li>Maintaining net revenue while reducing live weight gain will reduce the stress on the farm system </li></ul></ul><ul><ul><li>Profit obligations are met even as the farm operates below its carrying capacity </li></ul></ul><ul><ul><li>2. Prevent over-shoot </li></ul></ul><ul><ul><li>Real-time software allows frequent strategic re-evaluations </li></ul></ul><ul><ul><li>Frequent adjustments may help buffer against performance over-shoot </li></ul></ul>
107. 107. Applying an evolutionary metaheuristic <ul><ul><li>Implications for farm sustainability </li></ul></ul><ul><ul><li>1. Reduce stress </li></ul></ul><ul><ul><li>Maintaining net revenue while reducing live weight gain will reduce the stress on the farm system </li></ul></ul><ul><ul><li>Profit obligations are met even as the farm operates below its carrying capacity </li></ul></ul><ul><ul><li>2. Prevent over-shoot </li></ul></ul><ul><ul><li>Real-time software allows frequent strategic re-evaluations </li></ul></ul><ul><ul><li>Frequent adjustments may help buffer against performance over-shoot </li></ul></ul>
108. 108. Applying an evolutionary metaheuristic <ul><ul><li>Implications for farm sustainability </li></ul></ul><ul><ul><li>3. Providing choice </li></ul></ul><ul><ul><li>A Pareto set of optimal strategies gives the farmer a choice </li></ul></ul><ul><ul><li>Allowing the farm's objectives to be dynamically re-balanced </li></ul></ul><ul><ul><li>4. Enhancing control </li></ul></ul><ul><ul><li>Frequent re-evaluation plus a choice of optimal solutions gives the farmer flexibility </li></ul></ul><ul><ul><li>This allows control to be exerted in a measured and informed manner </li></ul></ul>
109. 109. Applying an evolutionary metaheuristic <ul><ul><li>Implications for farm sustainability </li></ul></ul><ul><ul><li>3. Providing choice </li></ul></ul><ul><ul><li>A Pareto set of optimal strategies gives the farmer a choice </li></ul></ul><ul><ul><li>Allowing the farm's objectives to be dynamically re-balanced </li></ul></ul><ul><ul><li>4. Enhancing control </li></ul></ul><ul><ul><li>Frequent re-evaluation plus a choice of optimal solutions gives the farmer flexibility </li></ul></ul><ul><ul><li>This allows control to be exerted in a measured and informed manner </li></ul></ul>
110. 110. Applying an evolutionary metaheuristic <ul><ul><li>Implications for farm sustainability </li></ul></ul><ul><ul><li>3. Providing choice </li></ul></ul><ul><ul><li>A Pareto set of optimal strategies gives the farmer a choice </li></ul></ul><ul><ul><li>Allowing the farm's objectives to be dynamically re-balanced </li></ul></ul><ul><ul><li>4. Enhancing control </li></ul></ul><ul><ul><li>Frequent re-evaluation plus a choice of optimal solutions gives the farmer flexibility </li></ul></ul><ul><ul><li>This allows control to be exerted in a measured and informed manner </li></ul></ul>
111. 111. Applying an evolutionary metaheuristic <ul><ul><li>Implications for farm sustainability </li></ul></ul><ul><ul><li>3. Providing choice </li></ul></ul><ul><ul><li>A Pareto set of optimal strategies gives the farmer a choice </li></ul></ul><ul><ul><li>Allowing the farm's objectives to be dynamically re-balanced </li></ul></ul><ul><ul><li>4. Enhancing control </li></ul></ul><ul><ul><li>Frequent re-evaluation plus a choice of optimal solutions gives the farmer flexibility </li></ul></ul><ul><ul><li>This allows control to be exerted in a measured and informed manner </li></ul></ul>
112. 112. Summary <ul><ul><li>Defined “metaheuristic” </li></ul></ul><ul><ul><li>Examined evolutionary algorithms </li></ul></ul><ul><ul><li>Explored problem constraints </li></ul></ul><ul><ul><li>Applied an evolutionary algorithm to a farm </li></ul></ul><ul><ul><li>Discussed the implications for farm sustainability </li></ul></ul>
113. 113. The End Images courtesy of http://www.goldennumber.net, Gary B. Meisner, Copyright 2006. Fibonacci numbers