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Autonomous Systems > Lecture 3 -Evolutionary Computation > Flashcards

Lecture 3 -Evolutionary Computation Flashcards

1
Q

Which mechanisms are at place between parents and offspring generation (2)?

A
  1. Recombination

2. (random) Mutation

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2
Q

First step of designing EA?
+ what does it need to be?
+ What forms can it take?

A

Designing a representation (phenotype) for an individual solution
+ that must be relevant to the problem we’re solving
+ it can be discrete value (binary, integer)

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3
Q

Example discrete representation

+ example mutation
-> how often does it happen?

A

Binary alphabet
10100011
-> single 0’s are genes, the whole 8 bit block is a chromosome

Mutation
10101011
-> based on certain probability for each gene

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4
Q

Example order based representation
+ What are individuals represented as?

+ What is it used for?

+ Example for mutation

A

Traveling salesman (city gets assigned number 1-N, solution could be 5,4,2,1,3 -> fasted way)

+Individuals are represented as permutations

+Used for ordering and sequencing problems

original 
1 2 3 4 5
mutation 
1 4 3 2 5
-> Swap
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5
Q

What is the most costly step for a real EA application?

-> What do we have to avoid?

A

Evaluating an individual

-> don’t re-evaluated unmodified individuals

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6
Q

Mutation operators

  1. One or many?
  2. What should it be able to reach?
  3. Size (2)
  4. Solutions
A
  1. At least one
  2. Should allow every part of the search/pattern space to be reached
  3. The size of mutation is important and should be controllable
  4. The mutation should produce valid solutions/chromosomes
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7
Q

Recombination operators

(3) important points
2. representation?

A
  1. Child should inherit from each parent
  2. The recombination operator should be designed in conjunction with the representation
  3. Recombination should produce valid chromosomes
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8
Q

Example Recombination for discrete representation

A 
Parent 1   Parent 2 
1 1 1 1 1      0 0 0 0 0 
cut at random point (n=3)
child 1       child 2
1 1 1 0 0    0 0 0 1 1
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9
Q

Selection strategy

  1. What do we want to ensure?
  2. What will this give us?
  3. What do we have to be careful of?
A
  1. Ensure that better individuals have higher chance of being parents
  2. this creates selection pressure
  3. less good individuals need some chance of being parents too - they might include useful genes
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10
Q

Example rank based selection

  1. What are individuals sorted on? What is this place called?
  2. How are individuals selected?
    - > example
A
  1. Sorted according to fitness value. The place in the list is called a rank.
  2. This rank is then used by a function that selects individuals. Individuals with higher ranks have a higher chance of being selected
    - > roulette
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11
Q

Replacement strategy

1. what does it affect? How so?

A

Deciding which individuals to kill effects the selection pressure

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12
Q

Elitism

A

Re-introducing the individual with the highest fitness in the population

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13
Q

Preservation

A

Keep individual with highest fitness in safe place

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14
Q

Recombination vs Mutations

  1. Recombination
    - what does it depend on?
    - what changes effect
    - ..
A
  • modifications depend on the whole population
  • it’s effect decreases with convergence
  • exploitation operator
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15
Q

Recombination vs Mutations

  1. Mutations
    - what is it good for?
    - strong…
    - …
A
  • Escaping the local optima
  • it has a strong causality principle
  • exploration operator
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16
Q

Stopping criterions (4)

A
  1. The optimum is reached
  2. Limit on CPU resources (there is a max amount of fitness evaluations)
  3. User’s patience
  4. Some generations without improvements
17
Q

Algorithm performance

  1. Design perspective
  2. Production perspective
  3. What should we never do?
A
  1. Find a very good solution at least once
  2. Find a good solution at almost every run
  3. Never draw conclusion from one run (use stats)
18
Q

Practical performance

  1. Acceptable performance at … on …
  2. Intrinsic…
A
  1. Acceptable performance at acceptable cost on a wide range of problems.
  2. Intrinsic parallelism (robustness, fault tolerance)
19
Q

At which problems is EA superior (3)?

A
  1. Lots of data with many free parameters
  2. Complex relationship between parameters
  3. Many (local) optima
20
Q

Key issue

1. Exploration

A

Sample unknown regions

If too much exploration (random search) there will be no convergence

21
Q

Key issue

2. Exploitation

A

Try to improve the best-so-far individuals
Too much of it leads to local search only.
The algorithm will converge to a local optimum.

Autonomous Systems (10 decks)

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