Genetic Simulated Annealing-Based Clustering Algorithm
- Login to Download
- 1 Credits
Resource Overview
A clustering algorithm integrating Genetic Algorithm (GA) with Simulated Annealing (SA) for enhanced optimization. By combining GA's population-based search and SA's probabilistic acceptance of suboptimal solutions, the algorithm effectively mitigates premature convergence issues in traditional GA. Key implementations include customized genetic encoding for cluster centers, a fitness function based on intra-cluster variance minimization, and adaptive cooling schedules, ensuring efficient convergence to global optima in clustering tasks.
Detailed Documentation
This paper presents a clustering algorithm based on a hybrid Genetic Simulated Annealing approach. The method synergistically combines Simulated Annealing (SA) and Genetic Algorithms (GA) to address clustering problems. The integration leverages SA's ability to escape local optima via controlled probability-based transitions and GA's parallel exploration of solution spaces, effectively overcoming premature convergence typical in standalone GA implementations. For clustering-specific adaptation, the algorithm employs a real-valued encoding scheme to represent cluster centroids, computes fitness using sum-of-squared-error minimization within clusters, and incorporates mutation operators with temperature-dependent acceptance criteria. These design choices enable faster and more reliable convergence to globally optimal clustering solutions, demonstrating robust performance in practical applications.
- Login to Download
- 1 Credits