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1 Solving the Generalized Graph Search Problem with Genetic Algorithms

This paper describes a genetic algorithm for finding low-cost paths through connectivity graphs. The map search problem does not lend itself to implementation with genetic algorithms because it is difficult to define a physically meaningful crossover operator: combining half of one set of driving directions with half of another set will rarely result in meaningful instructions. A selective crossover operator is used that will cross two fit solutions only if their paths intersect or come within one link of intersecting. The algorithm is demonstrated on a map of the bay area. The connectivity graph is automatically extracted from snapshots of the Yahoo! Maps database. Thousands of side-by-side snapshots are combined into a single 24000 x 37000 pixel image and automatically converted to a connectivity graph through various image processing techniques.