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Implications From the Analogous Relationship Between Evolutionary and Learning Processes
Jason Cheok Kuan Leong1, Masaaki Imaizumi2,3, Hideki Innan1
1Research Center for Integrative Evolutionary Science (RCIES), SOKENDAI, Hayama, Kanagawa, Japan.
Summary
Organismal evolution and machine learning share parallels, offering mutual benefits. This review advocates for interpretable machine learning to discover evolutionary laws and enhance predictive theory in biology.
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.
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