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ACIT4620 - Computational Intelligence > Module 4 - Lecture 10 - Evolutionary Computation > Flashcards

Module 4 - Lecture 10 - Evolutionary Computation Flashcards

1
Q

Evolutionary computation

What is an allele?

A

An allele is one of two or more versions of DNA sequence (a single base or a segment of bases) at a given genomic location.

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2
Q

Evolutionary computation

What is a trait?

A

A trait, as related to genetics, is a specific characteristic of an individual. (Phenotype?)

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3
Q

Evolutionary computation

What do traits affect? (2)

A

Survival and reproduction

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4
Q

Evolutionary computation

What does natural selection favor?

A

Good traits

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5
Q

Evolutionary computation

What is John Henry Holland known for?

A

Published a groundbreaking book on GA, “Adaptation in Natural and Artificial Systems”

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6
Q

Evolutionary computation

When was “Adaptation in Natural and Artificial Systems” published?

A

1975

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7
Q

Evolutionary computation

How are genes expressed (Data format)

A

Collection of bits, integers, real numbers etc.

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8
Q

Evolutionary computation

How is natural selection guided?

A

Through a fitness function

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9
Q

Evolutionary computation

What are some examples of mutation in a GA?

A

Bit flips, integer swaps, random pertubations etc.

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10
Q

Evolutionary computation

How is crossover typically performed?

A

Parents swap substrings

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11
Q

Evolutionary computation

What is the definition of GA? (Mathematical)

A

A probabilistic search algorithm which transforms a set of mathematical objects, each with a fitness value, into a new population using: Darwinian evolution and operations like crossover/mutation

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12
Q

Evolutionary computation

How is the initial population generated

A

Typically at random

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13
Q

Evolutionary computation

What does probabilistic selection mean?

A
  • The best are not always picked
  • The worst are not always excluded
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14
Q

Evolutionary computation

How do you choose whether to apply crossover or mutation?

A

Selected at random

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15
Q

Evolutionary computation

What is greedy exploitation?

A

Following the best solution only

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16
Q

Evolutionary computation

What is adventurous exploration?

A

Going through and exploring as many options as possible

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17
Q

Evolutionary computation

Does GA do greedy exploitation or adventurous exploration?

A

Yes, both.

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18
Q

Evolutionary computation

What is a chromosome?

A

A collection of genes, e.g. a position vector.

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19
Q

Evolutionary computation

What is a gene?

A

Part of a chromosome, e.g. one of the numbers in a position vector.

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20
Q

Evolutionary computation

What are the steps in a GA? (6) (CFNDIR)

A

1) Create a base population
2) Calculate fitness
3) Create new individuals based on chromosomes (e.g. Crossover, mutation)
4) Delete undesirable individuals.
5) Insert the new individuals into the population.
6) Repeat 2-6 until convergence.

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21
Q

Evolutionary computation

Who introduced particle swarm optimization (PSO)?

A

Kennedy and Eberhart

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22
Q

Evolutionary computation

When was PSO introduced?

A

1995

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23
Q

Evolutionary computation

What was the goal of PSO?

A

To develop a visual simulation of bird flocking behaviour.

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24
Q

Evolutionary computation

What is a particle (PSO)?

A

The position of one individual in a flock

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25
Q

Evolutionary computation

What are the 5 basic principles of swarm intelligence (SI)? PQDSA

A
  • Proximity
  • Quality
  • Diverse response
  • Stability
  • Adaptability
26
Q

Evolutionary computation

In SI, what is proximity?

A

agents can perform basic space and time calculations

27
Q

Evolutionary computation

In SI, what is Quality?

A

agents can respond to environmental conditions

28
Q

Evolutionary computation

In SI, what is “Diverse response”?

A

population can exhibit a wide range of behaviour

29
Q

Evolutionary computation

In SI, what is Stability?

A

Behaviour of population does not necessarily change every time the environment does

30
Q

Evolutionary computation

In SI, what is Adaptability?

A

The behaviour of a population must be able to adapt to the environment, if necessary.

31
Q

Evolutionary computation

What two vectors are associated with a PSO particle?

A

Position and velocity

32
Q

Evolutionary computation

What are the guiding signals a PSO particle receives?

A
  • Personal best
  • Global best
33
Q

Evolutionary computation

What is a PSO particle’s personal best?

A

the best value it has found so far (pbest – personal best) and its location

34
Q

Evolutionary computation

What is a PSO article’s global best?

A

the best value any individual in the population has found so far (gbest – global best) and its location

35
Q

Evolutionary computation

How does a PSO particle move?

A
  • Each particle has a velocity.
  • The velocity is updated based on its current velocity, the direction of its personal best position, and the global best known position.
36
Q

Evolutionary computation

What components does a PSO particle consist of? (3+2)

A

3 vectors: position, speed, personal best position
2 fitness values: current position, personal best

37
Q

Evolutionary computation

What is the cognition term of a PSO particle?

A

The velocity update contribution from its personal best.

38
Q

Evolutionary computation

What is the social term of a PSO article?

A

The velocity update contribution from its global best.

39
Q

Evolutionary computation

When updating a PSO particle’s position, what are c_1 and c_2?

A

Coefficients for the contribution of the cognition term/social best and social term/global best, respectively.

40
Q

Evolutionary computation

When updating a PSO particle’s position, what are r_1 and r_2?

A

Random contributions from the cognition term/social best and social term/global best, respectively.

41
Q

Evolutionary computation

What is the inertia weight?

A

A constant scalar (vector/matrix?) that decays the velocity of the particle to promote exploitation.

42
Q

Evolutionary computation

For PSO, What is the “The refined algorithm”?

A

Setting c_1 and c_2 (Contrib from pbest and gbest) fo 2, increasing exploration.

43
Q

Evolutionary computation

What are the termination conditions for PSO? (3)

A
  • Max number of iterations
  • Solution exceeds a quality threshold (good solution found)
  • Average velocity falls below some threshold.
44
Q

Evolutionary computation

What is ACO?

A

Ant colony optimization

45
Q

Evolutionary computation

What is the basic idea behind ant colony optimization (ACO)?

A

Ants communicate by leaving pheromone trails, signalling which paths are the most preferable.

46
Q

Evolutionary computation

What class of problems is ACO particularly suited for?

A

Discrete optimization problems

47
Q

Evolutionary computation

What is stigmergic communication?

A

Stigmergy is a communication method used in decentralized systems in which individuals communicate with each other by changing the surrounding environment.

48
Q

Evolutionary computation

Why does SI have better scalability?

A

Communication via the environment leads to less overhead; ants do not communicate with all other ants.

49
Q

Evolutionary computation

What is autocatalytic behavior?

A

Jumpstarting behavior. The more ants follow a trail, the more attractive that trail becomes for being followed.

50
Q

Evolutionary computation

Why is ACO a positive feedback loop?

A

The probability of a path choice increases with the # of times that path was previously chosen.

51
Q

Evolutionary computation

Who coined stigmergy?

A

Pierre-Paul Grasse

52
Q

Evolutionary computation

What is stigmergy?

A

Interaction through the environment

53
Q

Evolutionary computation

Is stigmergy direct or indirect?

A

Typically indirect.

54
Q

Evolutionary computation

What was ACO originally used for?

A

The travelling Salesman Problem (TSP).

55
Q

Evolutionary computation

Who designed the “Ant System”?

A

Marco Dorigo

56
Q

Evolutionary computation

When was the “Ant system” designed?

A

1992

57
Q

Evolutionary computation

What are the steps of the “Ant system”? (CMUEDR)

A

1) Create ants
2) Move ants
3) Update pheromone
4) Evaporate pheromones
5) Daemon activities
6) Repeat 2-5 until convergence

58
Q

Evolutionary computation

This is the transition probability function.

How does the parameter alpha impact the algorithm?

A

Alpha says how important pheromones are.

59
Q

Evolutionary computation

This is the transition probability function.

How does the parameters beta impact the algorithm?

A

Beta is the “Degree visibility”, i.e. how far away the ant can “look”.

60
Q

Evolutionary computation

In ACO, what is a daemon activity?

A

E.g. local search.

ACIT4620 - Computational Intelligence (7 decks)

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