Saturday 01 March 2025
A new approach to evolutionary algorithms, designed to balance exploration and exploitation, has been proposed by researchers. The method, which combines human intuition with artificial intelligence, shows promise in solving complex optimization problems.
Evolutionary algorithms are a type of computational optimization technique that mimics the process of natural selection. They have been widely used in various fields, including engineering, finance, and biology, to find the best solution among a large set of possibilities. However, these algorithms often struggle with balancing exploration and exploitation – exploring new solutions while also exploiting the most promising ones.
The proposed method, called Human-Centered Two-Phase Search (HCTPS), addresses this issue by dividing the optimization process into two phases: a global search phase and a local search phase. In the first phase, a human expert identifies the most promising regions of the search space using their intuition and domain knowledge. This information is then used to guide the algorithm in the second phase, where it performs a more detailed search within the selected regions.
The HCTPS framework consists of two main components: the Human Search Space Control (HSSCP) and the Genetic Algorithm (GA). The HSSCP is responsible for identifying the most promising subcubes in the search space, while the GA is used to perform the local search. The algorithm iteratively refines its search by selecting the best-performing solutions from each subcube and using them as a starting point for the next iteration.
The proposed method has been tested on 14 well-known benchmark problems, and the results show that it outperforms traditional evolutionary algorithms in terms of solution quality and computational efficiency. The algorithm’s ability to balance exploration and exploitation is particularly noteworthy, as it allows it to find high-quality solutions quickly while also exploring new regions of the search space.
The HCTPS framework has significant implications for various fields where optimization problems are common, such as engineering, finance, and biology. By combining human intuition with artificial intelligence, the algorithm can be used to solve complex real-world problems that require a deep understanding of the underlying domain.
In the future, researchers plan to further develop the HCTPS framework by incorporating additional features, such as parallelization and distributed computing. They also aim to apply the method to more challenging optimization problems and evaluate its performance in different domains.
Cite this article: “Human-Centered Two-Phase Search: A Novel Approach to Evolutionary Algorithms”, The Science Archive, 2025.
Optimization, Evolutionary Algorithms, Artificial Intelligence, Human-Centered Approach, Two-Phase Search, Global Search, Local Search, Genetic Algorithm, Solution Quality, Computational Efficiency
