The Pareto Front
Genetic Algorithms are a class of global optimizers that are particularly effective in cases where the objective function is a black box or is discrete and derivatives can't easily be calculated.. They are also effective in cases where the objective function is smooth or convex. It's just that in these cases, the availability of derivatives often makes other methods such as gradient descent faster. Sometimes, the target function might have competing objectives, for example designing a treatment plan that maximizes efficacy while minimizing adverse side-effects. In cases like this, we often want not a local or even global optimal solution, but also what are the tradeoffs among the goals. In other words, there’s no single “best” solution. Instead, you want the Pareto set : solutions where you can’t improve any objective without worsening another. The Pareto set or Pareto front consists of the possible solutions that are not dominated by any other solution. What we mean by...