[^argminmax]:In the metaheuristics literature, $\argmax$ is often assumed for evolutionary algorithms, whether $\argmin$ is often assumed for local search or simulated annealing.

Complete VS approximation VS heuristics.

Algorithmics

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>

> The better you understand it, the better the algorithm will be.

Naive algorithms

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- When is a random walk convergent?

- Example: 2D fixed-step size random walk.

Descent Algorithms

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Generic template:

```python

x=None

p=uniform(xmin,xmax)

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```

Greedy algorithm:

```python

defselect(x,p):

ifbetter(f(x),f(p)):

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What are the conditions for which a greedy algorithm would converge?

Simulated Annealing

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Generic template:

```python

P=uniform(xmin,xmax,n)

foriinrange(g):

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```

More complete template:

```python

defevol(f):

opt=float('inf')

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```

Evolution Strategies (ES), numerical space:

```python

defvariation(parents):

P=[]

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```

Genetic Algorithm (GA), boolean space:

```python

defvariation(parents):

crossed=crossover(parents)

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Is ES convergent?

If $$#P=1$$, what are the differences with a random walk?

Estimation of Distribution Algorithms

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