CFP last date
21 July 2025
Reseach Article

Generative AI’s Coming Dominance in Algorithm Design

by Rahul Karne, Akhil Dudhipala, Pavan Kumar Pativada
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 19
Year of Publication: 2025
Authors: Rahul Karne, Akhil Dudhipala, Pavan Kumar Pativada
10.5120/ijca2025925265

Rahul Karne, Akhil Dudhipala, Pavan Kumar Pativada . Generative AI’s Coming Dominance in Algorithm Design. International Journal of Computer Applications. 187, 19 ( Jul 2025), 27-33. DOI=10.5120/ijca2025925265

@article{ 10.5120/ijca2025925265,
author = { Rahul Karne, Akhil Dudhipala, Pavan Kumar Pativada },
title = { Generative AI’s Coming Dominance in Algorithm Design },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2025 },
volume = { 187 },
number = { 19 },
month = { Jul },
year = { 2025 },
issn = { 0975-8887 },
pages = { 27-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number19/generative-ais-coming-dominance-in-algorithm-design/ },
doi = { 10.5120/ijca2025925265 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-07-09T01:07:44.291292+05:30
%A Rahul Karne
%A Akhil Dudhipala
%A Pavan Kumar Pativada
%T Generative AI’s Coming Dominance in Algorithm Design
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 19
%P 27-33
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Generative AI (GenAI) is rapidly overtaking classical methods in algorithm design, driven by breakthroughs such as GANs, Transformers, and self-play reinforcement learning. We present a concise, controversial survey arguing that GenAI will become the primary driver of algorithmic innovation. To substantiate this claim, we include a quantitative meta-analysis of publication trends (2013–2022) demonstrating a 20× surge in GenAI research relative to traditional algorithm work. We compare GenAI-designed algorithms against human-crafted counterparts across interpretability, guarantees, adaptability, scalability, and development cycle (Table 1). We critically examine trade-offs—opacity, overfitting, ethical bias, and resource intensiveness—drawing on several highly cited ethics studies to highlight accountability and safety concerns. Finally, we outline future directions, advocating hybrid human–AI workflows, efficiency improvements, and robust governance to ensure GenAI’s advances remain aligned with societal values. This review’s bold stance and rich, high-impact references aim to catalyze debate and position the paper for widespread citation.

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Index Terms

Computer Science
Information Sciences

Keywords

Generative Artificial Intelligence Algorithm Design Machine Learning Transformers (Machine Learning) Deep learning Generative Adversarial Networks (GANs) Explainable Artificial Intelligence (XAI)