MATHEMATICAL OPTIMIZATION MODELS USED IN ARTIFICAL INTELLIGENCE ALGORITHMS


DOI:
https://doi.org/10.5281/zenodo.15714164Keywords:
Artifical intelligence, mathematics, optimizationAbstract
In this article, mathematical optimization techniques, which are one of the basic building blocks of artificial intelligence systems, are examined in detail. The place of optimization in artificial intelligence, basic concepts, classical and modern algorithms, application areas and current developments are evaluated. In addition, the role of optimization in the learning processes of artificial intelligence models and the comparison of different optimization methods are discussed. Finally, suggestions and research perspectives for the future of optimization in the field of artificial intelligence are presented.
References
Bäck, T. (1996). Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press.
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
Blum, C., & Roli, A. (2003). Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing Surveys, 35(3), 268-308.
Deb, K. (2001). Multi-Objective Optimization using Evolutionary Algorithms. Wiley.
Dorigo, M., & Stützle, T. (2004). Ant Colony Optimization. MIT Press.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Holland, J. H. (1992). Adaptation in Natural and Artificial Systems. MIT Press.
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN'95 - International Conference on Neural Networks, 4, 1942–1948.
Koller, D., & Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press.
Nocedal, J., & Wright, S. J. (2006). Numerical Optimization. Springer.
Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
Sivanandam, S. N., & Deepa, S. N. (2007). Introduction to Genetic Algorithms. Springer.
Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press.
Vapnik, V. N. (1998). Statistical Learning Theory. Wiley.
Wright, S., & Nocedal, J. (1999). Numerical optimization. Springer Science, 35(67-68), 7.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 ARCENG (INTERNATIONAL JOURNAL OF ARCHITECTURE AND ENGINEERING) ISSN: 2822-6895

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.