Exploring Different Types of Optimization

Looking into optimization techniques

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

In this lesson, we’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, we might find ourselves trying to balance work, relationships, health, fun, and sleep every day, which is a classic example of a multi-objective optimization problem. A multi-objective optimization problem is one in which there are multiple parameters or objective functions that need to be optimized. Oftentimes, but not always, the objective functions are in competition with one another. In other words, when we increase the value of one, the value of the other decreases.

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 objectives. It’s important to note that there isn’t a single global solution to a multi-objective optimization problem. Instead, the best solutions exist on a line representing a set of optimal solutions. We can intuitively think of this in the context of people balancing work, relationships, health, and so on. In this context, no single balance works for everybody, but instead, people determine what works best for them. There are multiple solutions to this type of problem.