Title :
Convergent analysis on evolutionary algorithm with non-uniform mutation
Author_Institution :
Sch. of Sci., Beijing Univ. of Posts & Telecommun., Beijing
Abstract :
Evolutionary algorithm (EA) with non-uniform mutation has the merits of even ldquolonger jumpsrdquo than Cauchy mutation at the early stage of the algorithm and much ldquofiner-tuningsrdquo than Gaussian mutation operator at the later stage. Empirical comparisons with the recently proposed EAs show its excellence solution quality and reliability. One unified algorithmic framework with non-uniform mutation operator and its convergence analysis based on this algorithmic framework are provided in this paper. Two lemmas and two theorems are presented to show the relevant convergence properties of unimodal and multimodal functions.
Keywords :
convergence; evolutionary computation; convergence analysis; evolutionary algorithm; nonuniform mutation operator; Algorithm design and analysis; Biological information theory; Encoding; Evolution (biology); Evolutionary computation; Genetic algorithms; Genetic mutations; Genetic programming; Probability distribution; Stochastic processes; evolutionary algorithm; non-uniform mutation; stochastic process; theoretical analysis;
Conference_Titel :
Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1822-0
Electronic_ISBN :
978-1-4244-1823-7
DOI :
10.1109/CEC.2008.4630909