Directed evolution in AI leverages the principles of biological evolution—such as mutation, selection, and inheritance—to develop algorithms that gradually evolve to solve a problem more efficiently. This approach is part of a broader category of evolutionary computing and is useful in situations where the optimal solution is difficult to reach through conventional algorithmic approaches. By simulating the process of natural selection, directed evolution allows for the exploration of a vast space of potential solutions, iteratively selecting and modifying the best performers for further improvement.
The concept of applying evolutionary principles to computer science dates back to the 1950s and 1960s, but the specific term "directed evolution" is more commonly associated with its biological counterpart. In AI, similar methodologies began gaining prominence under the terms "genetic algorithms" and "evolutionary computing" in the 1970s and 1980s.
While the term "directed evolution" is traditionally linked to Frances Arnold in the context of chemistry and molecular biology, in AI, the foundational work by John Holland on genetic algorithms and evolutionary computing in the 1960s and 1970s laid the groundwork for what might be considered directed evolution in AI today.