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A Unified Framework for Self-Learning AI: Reinforcement Learning, Neural Search, and Adaptive Evolution

by Praveen Kumar Myakala, Chiranjeevi Bura, Anil Kumar Jonnalagadda, Praveen Chaitanya Jakku
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 67
Year of Publication: 2025
Authors: Praveen Kumar Myakala, Chiranjeevi Bura, Anil Kumar Jonnalagadda, Praveen Chaitanya Jakku
10.5120/ijca2025924503

Praveen Kumar Myakala, Chiranjeevi Bura, Anil Kumar Jonnalagadda, Praveen Chaitanya Jakku . A Unified Framework for Self-Learning AI: Reinforcement Learning, Neural Search, and Adaptive Evolution. International Journal of Computer Applications. 186, 67 ( Feb 2025), 9-20. DOI=10.5120/ijca2025924503

@article{ 10.5120/ijca2025924503,
author = { Praveen Kumar Myakala, Chiranjeevi Bura, Anil Kumar Jonnalagadda, Praveen Chaitanya Jakku },
title = { A Unified Framework for Self-Learning AI: Reinforcement Learning, Neural Search, and Adaptive Evolution },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2025 },
volume = { 186 },
number = { 67 },
month = { Feb },
year = { 2025 },
issn = { 0975-8887 },
pages = { 9-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number67/a-unified-framework-for-self-learning-ai-reinforcement-learning-neural-search-and-adaptive-evolution/ },
doi = { 10.5120/ijca2025924503 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-02-25T22:58:01.876833+05:30
%A Praveen Kumar Myakala
%A Chiranjeevi Bura
%A Anil Kumar Jonnalagadda
%A Praveen Chaitanya Jakku
%T A Unified Framework for Self-Learning AI: Reinforcement Learning, Neural Search, and Adaptive Evolution
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 67
%P 9-20
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This study explores the shift from explicitly programmed systems to machines capable of autonomous learning and adaptation, addressing the scalability and flexibility limitations of traditional programming. By integrating advanced machine learning, reinforcement learning, and self-evolving algorithms, this study aims to establish principles that enable machines to process information autonomously, adapt behaviors, and operate in dynamic, unstructured environments. Key challenges, such as ensuring system safety and robustness, are examined along with practical applications in robotics, personalized healthcare, and adaptive AI systems. This study lays the foundation for next-generation adaptive agents, providing a transformative framework to achieve true autonomy in artificial intelligence.

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Index Terms

Computer Science
Information Sciences
Artificial Intelligence
Autonomous Systems
Machine Learning
Adaptive Algorithms
Reinforcement Learning
Self-Evolving Systems
Neural Networks
Robotics
Data-Driven Decision Making
Emergent Behavior

Keywords

Autonomous Machine Intelligence Reinforcement Learning Self-Evolving Algorithms Adaptive AI Systems Neural Architecture Search

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