I will present two projects demonstrating the interplay between growth and dispersal in evolutionary contexts. In the first one, I focus on the evolution of expanding populations, such as bacterial colonies or cancerous tumors. If population expansion is driven by growth and dispersal, then the evolutionary fate of a mutant also should also on both growth and dispersal. But when could moving faster and growing slower be a favorable strategy? Starting with the Fisher-KPP equation, I come up with a quantitative evolutionary rule, which depends on growth and dispersal rates of the ancestor and the mutant. Theoretical findings are supported by experiments with bacterial swarming colonies, as well as data from ecology literature. In the second project, I address the question of design optimization of a biochemical network (the C-di-GMP network in bacteria) that integrates signals from the environment and regulates genes for growth and dispersal. This network needs to be trained to provide the optimal outcome when the environment alternatively favors growth or dispersal. I show that natural selection in this network architecture is mathematically equivalent to training a machine learning model to solve a classification problem.