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Evolutionary Algorithms > Lecture 1 > Flashcards

Lecture 1 Flashcards

1
Q

What are the three main ingredients for EA?

A

1) Have a Populations
2) Select better Individuals
3) Perform Variation

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2
Q

Describe the psuedo code for general EA’s

A

1 𝑑 ← 0
2 𝑷𝑑← π‘π‘Ÿπ‘’π‘Žπ‘‘π‘’π΄π‘›π‘‘πΈπ‘£π‘Žπ‘™π‘’π‘Žπ‘‘π‘’πΌπ‘›π‘–π‘‘π‘–π‘Žπ‘™πΌπ‘›π‘‘π‘–π‘£π‘–π‘‘π‘’π‘Žπ‘™π‘  𝑛
3 π’˜π’‰π’Šπ’π’† π‘‘π‘’π‘Ÿπ‘šπ‘–π‘›π‘Žπ‘‘π‘–π‘œπ‘›πΆπ‘Ÿπ‘–π‘‘π‘’π‘Ÿπ‘–π‘œπ‘›π‘π‘œπ‘‘π‘†π‘Žπ‘‘π‘–π‘ π‘“π‘–π‘’π‘‘(𝑷𝑑) 𝒅𝒐
3.1 𝑢𝑑 ← π‘π‘Ÿπ‘’π‘Žπ‘‘π‘’π΄π‘›π‘‘πΈπ‘£π‘Žπ‘™π‘’π‘Žπ‘‘π‘’π‘‚π‘“π‘“π‘ π‘π‘Ÿπ‘–π‘›π‘”(𝑷𝑑)
3.2 𝑷𝑑+1 ← π‘ π‘’π‘™π‘’π‘π‘‘π‘†π‘’π‘Ÿπ‘£π‘–π‘£π‘–π‘›π‘”πΌπ‘›π‘‘π‘–π‘£π‘–π‘‘π‘’π‘Žπ‘™π‘ (𝑷𝑑, 𝑢𝑑)
3.3 𝑑 ← 𝑑 + 1

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3
Q

What is a Black Box Optimalisation perspective?

A

The Black Box Optimization (BBO) perspective:
– Maximize F(x), x ∈ S
– No prior knowledge of F
– Guess new x and evaluate
– Minimize time (or number of evaluations)

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4
Q

What is a White Box Optimalisation perspective?

A

The White Box Optimization (WBO) perspective:
– Everything known and sufficiently understood
– Use knowledge to your advantage
* Solve analytically
* Build problem-specific algorithms

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5
Q

Which is better on complex problems, WBO or BBO?

A

BBO

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6
Q

Which is better on simple problems, WBO or BBO?

A

WBO

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7
Q

For which case is optimum guaranteed? WBO, BBO or both?

A

only WBO

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8
Q

What is the problem with WBO compared to BBO?

A

Not well generalisable

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9
Q

Can optimum be guaranteed in BBO?

A

Only in infinite runs and infinite time.

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Evolutionary Algorithms (9 decks)

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