+Introduction

In this session we review the fundamentals of the Genetic Algorithm (GA) and its context within the broader field of optimization. The power of the GA is its simplicity, as it is composed of only four elements: generation, selection, crossover, and mutation. In these lectures, we review these elements and detail and discuss how they are implemented in Discover. This will give you an intuition for what is happening "under the hood" when you run an optimization in Discover, which will allow you to make better decisions when designing your generative models.

+Elements of the GA - Generation and Selection

+Elements of the GA - Crossover and Mutation

+Evaluating generative models

+Troubleshooting

+Five guidelines for designing better generative models