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Neuroevolution in Artificial Intelligence
Subject area: Science,Engineering and Technology · Area of research: Engineering and Artificial Intelligence
Abstract
Neuroevolution, the amalgamation of neural networks with evolutionary algorithms, stands as a transformative force in advancing Artificial Intelligence (AI). This paper unfolds with the purpose of elucidating the fundamental concepts and applications of Neuroevolution, aiming to provide a nuanced understanding of its significance in propelling the field of AI. Beginning with an exploration of the synergies between evolutionary algorithms and neural networks, the paper emphasizes the overarching objective of showcasing the real-world applicability of Neuroevolution in solving intricate problems across diverse domains. Evolving architectures of neural networks, including the adaptability in Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks, are examined to elucidate the adaptability intrinsic to Neuroevolution. The paper delves into scalability and efficiency strategies, shedding light on handling larger neural network architectures and enhancing computational efficiency. Integration into multi-agent systems is explored, emphasizing Neuroevolution's role in optimizing cooperative and competitive behaviors within complex interactions. Robustness and adaptability analysis of Neuroevolved networks form a critical aspect, evaluating their resilience in varied conditions and their generalization capabilities. Conclusively, the paper outlines the contributions of Neuroevolution to the broader AI landscape, providing insights for researchers and practitioners and fostering developments at the intersection of neural networks and evolutionary algorithms.
Keywords
Neuroevolution, Artificial Intelligence, Evolutionary Algorithms, Neural Networks, Genetic Algorithms, Learning Algorithms, Optimization Techniques, Machine Learning, Reinforcement Learning, Evolutionary Strategies
How to cite this paper
@article{1705504,
author = {Prajwal Pawar, Prof. Punam Shinde},
title = {Neuroevolution in Artificial Intelligence},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {7},
number = {8},
pages = {189-197},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1705504.pdf},
abstract = {Neuroevolution, the amalgamation of neural networks with evolutionary algorithms, stands as a transformative force in advancing Artificial Intelligence (AI). This paper unfolds with the purpose of elucidating the fundamental concepts and applications of Neuroevolution, aiming to provide a nuanced understanding of its significance in propelling the field of AI. Beginning with an exploration of the synergies between evolutionary algorithms and neural networks, the paper emphasizes the overarching objective of showcasing the real-world applicability of Neuroevolution in solving intricate problems across diverse domains. Evolving architectures of neural networks, including the adaptability in Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks, are examined to elucidate the adaptability intrinsic to Neuroevolution. The paper delves into scalability and efficiency strategies, shedding light on handling larger neural network architectures and enhancing computational efficiency. Integration into multi-agent systems is explored, emphasizing Neuroevolution's role in optimizing cooperative and competitive behaviors within complex interactions. Robustness and adaptability analysis of Neuroevolved networks form a critical aspect, evaluating their resilience in varied conditions and their generalization capabilities. Conclusively, the paper outlines the contributions of Neuroevolution to the broader AI landscape, providing insights for researchers and practitioners and fostering developments at the intersection of neural networks and evolutionary algorithms.},
keywords = {Neuroevolution, Artificial Intelligence, Evolutionary Algorithms, Neural Networks, Genetic Algorithms, Learning Algorithms, Optimization Techniques, Machine Learning, Reinforcement Learning, Evolutionary Strategies},
month = {February},
}