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Table 1 Comparison of different algorithms on performance, convergence and stability. Five apporaches are compared based on time performance, convergence and stability. The K-means algorithm has better time performance than any other genetic algorithms, but it suffers from converging to local optimum and initialization dependent. Among the four genetic clustering approaches, Hybrid approach always has better time performance while FGKA performs well when the mutation probability is big, and IGKA performs well when the mutation probability is small. IGKA and FGKA outperform GKA. The convergence of four genetic algorithms has similar results, and all four are independent from the initialization.

From: Incremental genetic K-means algorithm and its application in gene expression data analysis

  K-means GKA FGKA IGKA Hybrid
Time Fastest Slow Good when the mutation Good when the mutation Good
Performance    probability is large probability is small  
Convergence Worse Good Good Good Good
Stability Unstable Stable Stable Stable Stable