Original scientific paper
The Island Model Genetic Algorithm: On Separability, Population Size and Convergence
Darrell Whitley
; Department of Computer Science, Colorado State University, Fort Collins. Colorado, USA
Soraya Rana
; Department of Computer Science, Colorado State University, Fort Collins. Colorado, USA
Robert B. Heckendorn
; Department of Computer Science, Colorado State University, Fort Collins. Colorado, USA
Abstract
Parallel Genetic Algorithms have often been reported to yield better performance than Genetic Algorithms which use a single large panmictic population. In the case of the Island Model genetic algorithm, it has been informally argued that having multiple subpopulations helps to preserve genetic diversity, since each island can potentially follow a different search trajectory through the search space. It is also possible that since linearly separable problems are often used to test Genetic Algorithms, that Island Models may simply be particularly well suited to exploiting the separable nature of the test problems. We explore this possibility by using the infinite population models of simple genetic algorithms to study how Island Models can track multiple search trajectories. We also introduce a simple model for better understanding when Island Model genetic algorithms may have an advantage when processing some test problems. We provide empirical results for both linearly separable and nonseparable parameter optimization functions.
Keywords
Hrčak ID:
150198
URI
Publication date:
30.3.1999.
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