Implications From the Analogous Relationship Between Evolutionary and Learning Processes.

Area of Science:

• Evolutionary Biology
• Machine Learning
• Computational Biology

Background:

• Organismal evolution involves generational trial-and-error for improved phenotypes.
• The analogy between evolution and machine learning (ML) has been recognized since the 1950s.
• Significant opportunities exist for interdisciplinary research between evolutionary biology and ML.

Purpose of the Study:

• To explore conceptual parallels between evolutionary biology and ML.
• To enhance predictive capabilities and theoretical frameworks in both fields.
• To advocate for interpretable ML for discovering generalizable evolutionary laws.

Main Methods:

• Review of recent advances in evolutionary biology and ML.
• Analysis of conceptual parallels and their implications.
• Focus on interpretable ML for predictive modeling in evolution.

Main Results:

• Identified significant conceptual overlaps between evolutionary processes and ML algorithms.
• Highlighted the potential of interpretable ML to uncover fundamental laws governing evolutionary outcomes.
• Demonstrated how ML can advance theoretical biology and inspire new algorithms.

Conclusions:

• Integrating interpretable ML can transform evolutionary science into a predictive field.
• This interdisciplinary approach fosters mutual advancement in biology and computer science.
• Future research should focus on developing theoretical frameworks for evolutionary prediction using ML.