Application of Evolutionary Intelligence in Scientific Discovery
Artificial Intelligence (AI) is shifting scientific discovery from task-specific workflows to autonomous systems that organize exploration with experimental and human feedback in open candidate spaces. Evolutionary Computation (EC) provides a computational basis for feedback-driven discovery, as population-based search can maintain diverse scientific candidates while steering exploration through accumulated evidence.
However, EC primarily focuses on candidate refinement for predefined problems, while cumulative discovery requires experience retention. To bridge this gap, this review introduces Evolutionary Intelligence (EI) for scientific discovery. EI characterizes scientific AI systems that sustain exploration by linking candidate refinement with experience retention across evolutionary cycles.
We introduce a five-dimensional analytical framework that addresses the following questions:
- What evolves?
- How do candidates change?
- Why are candidates selected?
- Where does feedback originate?
- When does evolution occur?
This framework clarifies how EI transforms isolated search trajectories into cumulative scientific insight. We further demonstrate this paradigm across diverse discovery modes, from evolving concrete scientific entities to orchestrating automated research workflows.
Finally, we identify critical bottlenecks regarding evaluation, process traceability, and shared infrastructure, providing a concrete roadmap for advancing the transition from EC to EI in scientific discovery.
Blogger's Review: The introduction of evolutionary intelligence offers a fresh perspective on scientific discovery, emphasizing the integration of exploration and experience. The five-dimensional framework allows for a clearer understanding of the dynamic processes in scientific discovery, and future research will benefit from this innovative thinking that transcends traditional methodologies.