Skewed Crossover and the Dynamic Distributed Database Problem
The automatic self-management of large, distributed databases is a significant problem area for providers of global management information systems and services. Finding a way of dynamically balancing changing load over a number of globally distributed servers can be an arduous task, particularly when communications costs and overheads are also considered. Previous work has shown that this problem can prove a difficult search space to negotiate for Genetic Algorithms. This paper introduces a skewed form of 2-point crossover which appears to give exceedingly encouraging results, particularly on scenarios which have previously been categorised as ‘problematic’. Whilst the advantages of this form of crossover may prove to be problem (or even solution) specific, it is likely to be of use in problems where sub-sequence information is an important feature of schemata (such as scheduling problems), or where optimal solutions contain repetition of either individual or short sequences of alleles across numerous gene positions. The technique effectively provides an additional, orthogonal source of genetic diversity, apparently reducing the need for either excessive initial population size or high levels of mutation. It is also shown to be effective as part of the mutation operator in Simulated Annealing.
KeywordsGenetic Algorithm Mutation Rate Communication Cost Crossover Operator Database Design
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