Investigating adaptive mutation in the generalized generation gap (G3) algorithm for unconstrained global optimization

Teo, Jason Tze Wi (2007) Investigating adaptive mutation in the generalized generation gap (G3) algorithm for unconstrained global optimization. In: 2007 ACM Symposium on Applied Computing, 11-15 March 2007, Seoul, South Korea.

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Official URL: http://dx.doi.org/10.1145/1244002.1244167

Abstract

For function optimization problems in continuous search spaces, one of the main difficulties currently faced is that of locating high quality solutions. This problem is particularly pertinent for continuous multimodal problems where the quality rather than computational efficiency is more important as a test of the solver's ability to escape local optima and finding solutions near the global optimum. Moreover, this difficulty is further compounded when the function involves large numbers of variables, which translates into a highly deceptive fitness landscape with very large numbers of local optima.

Item Type:Conference Paper (UNSPECIFIED)
Uncontrolled Keywords:Adaptive mutation, G3, Real-coded genetic algorithms
Subjects:?? QA75.5-76.95 ??
Divisions:SCHOOL > School of Engineering and Information Technology
ID Code:2856
Deposited By:IR Admin
Deposited On:21 Apr 2011 17:22
Last Modified:29 Dec 2014 16:53

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