Simulated annealing (SA) is a probabilistic technique for approximating the global optimum of a given function. Specifically, it is a metaheuristic to approximate global optimization in a large search space for an optimization problem. It is often used when the search space is discrete (e.g., the traveling salesman problem). For problems where finding an approximate global optimum is more important than finding a precise local optimum in a fixed amount of time, simulated annealing may be preferable to exact algorithms such as gradient descent, Branch and Bound.

https://en.wikipedia.org/wiki/Simulated_annealing

Annealing ? WTF ?

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Simulated Annealing :: What it does

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S.A. exercise: Rocket launch revisited

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https://bleuje.github.io/p5js-myprojects/rocket-optimization-sa/index.html

S.A. How & why it works

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Simulated Annealing :: Exercise

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You will be divided into two break-out rooms, each group will include at least two Python ninjas.

Task: transform Maurice's Genetic algorithm for detection of furthest point into Simulated Annealing form

Hint: check https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.optimize.anneal.html