Session:

Evolutionary Computation and Neural Networks (ECNN)
Monday March 01, 2004, 17.55 – 18.15

Session Chair:

Paulo Cortez, Miguel Rocha

Paper Title:

Genetic Algorithms with Fitness & Diversity –Guided Adaptive Operating Probabilities and Analyses of its Convergence

Author(s):

L. Meiyi, College of Information Science & Engineering, Central South University , Changsha, China
C. Zixing, College of Information Science & Engineering, Central South University , Changsha, China
S. Guoyun, College of Information Science & Engineering, Central South University , Changsha, China

Abstract:

The paper has analyzed global convergence properties of adaptive genetic algorithms combining adaptive probabilities of crossover and mutation with diversity-guided crossover and mutation. By means of homogeneous finite Markov chains, it is proved that AGAD, which is present in this paper, and GAD (genetic algorithms with diversity-guided mutation) maintaining the best solution converge to the global optimum, which is the main contributions of this paper. The performance of AGA(adaptive genetic algorithms with adaptive probabilities of crossover and mutation), GAD and AGAD in optimizing several unimodal and multimodal functions has been compared. For multimodal functions, the AGAD converges to the global optimum for fewer generations than AGA and GAD, and it hardly has premature convergence.