William C. Liles

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Although a variety of coevolutionary methods have been explored over the years, it has only been recently that a general architecture for cooperative coevolution has been proposed. Since that time, the flexibility and success of this cooperative coevolutionary architecture (CCA) has been shown in an array of different kinds of problems. However, many(More)
— The task of understanding coevolutionary algorithms is a very difficult one. These algorithms search landscapes which are in some sense adaptive. As a result, the dynamical behaviors of coevolu-tionary systems can frequently be even more complex than traditional evolutionary algorithms (EAs). Moreover, traditional EA theory tells us little about(More)
Though coevolutionary algorithms are currently used for optimization purposes, practitioners are often plagued with difficulties due to the fact that such systems frequently behave in counter intuitive ways that are not well understood. This paper seeks to extend work which uses evolutionary game theory (EGT) as a form of dynamical systems mod-eling of(More)
Cooperative coevolutionary algorithms (CCEAs) have been applied to many optimization problems with varied success. Recent empirical studies have shown that choices surrounding methods of collaboration may have a strong impact on the success of the algorithm. Moreover, certain properties of the problem landscape, such as variable interaction, greatly(More)
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