On the Parallels Between Evolutionary Theory and the State of AI

This article critically examines the foundational principles of contemporary AI methods, exploring the limitations that hinder its potential. We draw parallels between the modern AI landscape and the 20th-century Modern Synthesis in evolutionary biology, and highlight how advancements in evolutionary theory that augmented the Modern Synthesis, particularly those of Evolutionary Developmental Biology, offer insights that can inform a new design paradigm for AI. By synthesizing findings across AI and evolutionary theory, we propose a pathway to overcome existing limitations, enabling AI to achieve its aspirational goals.

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Publication Details

Journal
Proceedings of the Genetic and Evolutionary Computation Conference Companion
Published
2025-07-14
DOI
https://doi.org/10.1145/3712255.3734320
Primary Topic
Evolutionary Algorithms and Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

On the Parallels Between Evolutionary Theory and the State of AI

Zeki Doruk Erden, Boi V. Faltings
Proceedings of the Genetic and Evolutionary Computation Conference Companion
Evolutionary Algorithms and Applications
article

On the Parallels Between Evolutionary Theory and the State of AI

Zeki Doruk Erden, Boi V. Faltings
article en

Abstract

This article critically examines the foundational principles of contemporary AI methods, exploring the limitations that hinder its potential. We draw parallels between the modern AI landscape and the 20th-century Modern Synthesis in evolutionary biology, and highlight how advancements in evolutionary theory that augmented the Modern Synthesis, particularly those of Evolutionary Developmental Biology, offer insights that can inform a new design paradigm for AI. By synthesizing findings across AI and evolutionary theory, we propose a pathway to overcome existing limitations, enabling AI to achieve its aspirational goals.

Proceedings of the Genetic and Evolutionary Computation Conference Companion
École Polytechnique Fédérale de Lausanne (CH)
Openalex Percentile: Top 62%
Evolutionary Algorithms and Applications
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