UMDAs for dynamic optimization problems

10 years 21 days ago
UMDAs for dynamic optimization problems
This paper investigates how the Univariate Marginal Distribution Algorithm (UMDA) behaves in non-stationary environments when engaging in sampling and selection strategies designed to correct diversity loss. Although their performance when solving Dynamic Optimization Problems (DOP) is less studied than populationbased Evolutionary Algorithms, UMDA and other Estimation of Distribution Algorithms may follow similar schemes when tracking moving optima: genetic diversity maintenance, memory schemes, niching methods, and even reinicialization of the probability vectors. This study is focused on diversity maintenance schemes. A new update strategy for UMDA’s probability model, based on Ant Colony Optimization transition probability equations, is presented and empirically compared with other strategies recently published that aim to correct diversity loss in UMDA. Results demonstrate that loss correction strategies delay or avoid full convergence, thus increasing UMDA’s adaptability to ...
Carlos M. Fernandes, Cláudio F. Lima, Agost
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2008
Authors Carlos M. Fernandes, Cláudio F. Lima, Agostinho C. Rosa
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