Charilogis, V.; Tsoulos, I.G.; Stavrou, V.N. An Intelligent Technique for Initial Distribution of Genetic Algorithms. Axioms2023, 12, 980.
Charilogis, V.; Tsoulos, I.G.; Stavrou, V.N. An Intelligent Technique for Initial Distribution of Genetic Algorithms. Axioms 2023, 12, 980.
Charilogis, V.; Tsoulos, I.G.; Stavrou, V.N. An Intelligent Technique for Initial Distribution of Genetic Algorithms. Axioms2023, 12, 980.
Charilogis, V.; Tsoulos, I.G.; Stavrou, V.N. An Intelligent Technique for Initial Distribution of Genetic Algorithms. Axioms 2023, 12, 980.
Abstract
The need to find the global minimum in multi-dimensional functions is a critical problem in many fields of science and technology. Effectively solving this problem requires the creation of initial solution estimates, which are subsequently used by the optimization algorithm to search for the best solution in the solution space. In the context of this article, a novel approach to generating the initial solution distribution is presented which is applied to a genetic optimization algorithm. Using the k-means clustering algorithm, a distribution based on data similarity is created. This helps in generating initial estimates that may be more tailored to the problem. Additionally, the proposed method employs a rejection sampling algorithm to discard samples that do not yield better solution estimates in the optimization process. This allows the algorithm to focus on potentially optimal solutions, thus improving its performance. Finally, the article presents experimental results from the application of this approach to various optimization problems, providing the scientific community with a new method for addressing this significant problem.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
