Chapter 5. Optimization

This chapter will look at how to solve collaboration problems using a set of techniques called stochastic optimization. Optimization techniques are typically used in problems that have many possible solutions across many variables, and that have outcomes that can change greatly depending on the combinations of these variables. These optimization techniques have a wide variety of applications: we use them in physics to study molecular dynamics, in biology to predict protein structures, and in computer science to determine the worst possible running time of an algorithm. NASA even uses optimization techniques to design antennas that have the right operating characteristics, which look unlike anything a human designer would create.

Optimization finds the best solution to a problem by trying many different solutions and scoring them to determine their quality. Optimization is typically used in cases where there are too many possible solutions to try them all. The simplest but least effective method of searching for solutions is just trying a few thousand random guesses and seeing which one is best. More effective methods, which will be discussed in this chapter, involve intelligently modifying the solutions in a way that is likely to improve them.

The first example in this chapter concerns group travel planning. Anyone who has planned a trip for a group of people, or perhaps even for an individual, realizes that there are a lot of different inputs required, such ...

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