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Summary: 550 IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, VOL. 10, NO. 5, OCTOBER 2006
Graph-Based Evolutionary Algorithms
Kenneth Mark Bryden, Daniel A. Ashlock, Steven Corns, and Stephen J. Willson
Abstract--Evolutionary algorithms use crossover to combine in-
formation from pairs of solutions and use selection to retain the
best solutions. Ideally, crossover takes distinct good features from
each of the two structures involved. This process creates a conflict:
progress results from crossing over structures with different fea-
tures, but crossover produces new structures that are like their par-
ents and so reduces the diversity on which it depends. As evolution
continues, the algorithm searches a smaller and smaller portion of
the search space. Mutation can help maintain diversity but is not
a panacea for diversity loss. This paper explores evolutionary al-
gorithms that use combinatorial graphs to limit possible crossover
partners. These graphs limit the speed and manner in which infor-
mation can spread giving competing solutions time to mature. This
use of graphs is a computationally inexpensive method of picking a
global level of tradeoff between exploration and exploitation. The
results of using 26 graphs with a diverse collection of graphical
properties are presented. The test problems used are: one-max, the
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