👉 Breeding computing, also known as computational breeding or evolutionary computation, is a method that combines principles from genetic algorithms and evolutionary strategies with traditional breeding techniques in agriculture and biotechnology. It involves using computational models to simulate the process of natural selection, where desirable traits are identified and propagated through successive generations of "breeding" individuals (often represented as genetic combinations or phenotypes). By iteratively selecting, crossing, and mutating these individuals based on predefined fitness criteria, breeding computing aims to optimize the genetic makeup of organisms for specific traits such as increased yield, disease resistance, or environmental adaptability. This approach allows researchers to explore a vast genetic space efficiently, accelerating the development of improved crop varieties and livestock breeds without the need for extensive and time-consuming physical breeding programs.
