Lecture 7 Flashcards

1
Q

what is the convergence velocity?

A

the expectation of the distance towards the optimum covered per generation

phi = E[|f(x) - f(xt)| - |f(x) - f(x{t+1})|]

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2
Q

what is the 1/5 success rule?

A

the optimal success probability should be about 1/5. If, during execution of (1+1)-ES, it is larger than 1/5, increase sigma, if smaller than 1/5, decrease sigma

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3
Q

for the corridor and the sphere model, what are the optimal standard deviations?

A

corridor: sigma’_opt = sqrt(2pi)
sphere: sigma’_opt = 1.224

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4
Q

what is the corridor model?

A

corridor represents a range in the solution space where the algorithm focuses its search
- the width influences the exploration behaviour of the algorithm
- as the optimization progresses, the corridor narrows
- wider corridor => slower convergence

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5
Q

what are the maximum convergence velocities for the corridor and the sphere model?

A

corridor: 1/e
sphere: 0.2025

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6
Q

what are the optimal success probabilities for the corridor and the sphere model?

A

corridor: 1/(2e)
sphere: 0.27

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7
Q

what is the sphere model?

A

a simplified optimization landscape with a convex, unimodal objective function that provides an environment for evaluating the performance

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8
Q

what is the formula to calculate the convergence velocity for the sphere model?

A

phi = E(R^2 - r^2)
R is the radius of the sphere that represents all the possible definitions of the parent, r is the radius of the sphere that represents all possible definitions of the offspring

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9
Q

why would you measure the fixed-target running time?

A

measure the cost, quantitative comparison

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10
Q

what is the advantage of assessing the fixed-budget objective value?

A

directly applicable to runs with a very small number of function evaluations

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11
Q

how do you calculate the expected running time of an algorithm?

A

divide the amount of evaluations by the success rate

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12
Q

what is neuroevolution?

A

the process of automatically configuring and training artificial neural networks using EAs

part of Neural Architecture Search (NAS)

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13
Q

what are the tree main components of NAS models?

A
  1. search space
  2. search strategy
  3. performance estimation strategies
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