Exploring Different Types of Optimization

Genetic Algorithms in Elixir — by Sean Moriarity (41 / 101)

👈 Crafting Fitness Functions | TOC | What You Learned 👉

So far, the problems you’ve implemented in this book have focused on optimizing a single objective using a simple fitness function. In the real world, some of the problems you’ll encounter will be much more complex.

In this section, you’ll briefly explore two classes of optimization that require more advanced approaches to evaluation: multi-objective optimization and interactive optimization.

Optimizing Multiple Objectives

The real world is full of competing interests that need to be optimized. For example, you might find yourself trying to balance work, relationships, health, fun, and sleep every day — a classic example of a multi-objective optimization problem. A multi-objective optimization problem is one in which you have multiple parameters or objective functions that need to be optimized. Oftentimes, but not always, the objective functions are in competition with one another — when you increase the value of one, the value of the other goes down.

Multi-objective optimization problems are some of the most common problems that appear in the real world. They also can be the most difficult to solve because they require a means of balancing your objectives. It’s important to note that there isn’t a single global solution to a…