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Genetic Algorithms are heuristic search schemes based on a model of Darwinian evolution. Although not guaranteed to find the optimal solution, genetic algorithms have been shown to be effective at finding near optimal and, in some cases, optimal solutions to combinatorially explosive problems. Finding a maximal length snake, a list of vertices satisfying… (More)

The snake-in-the-box problem is a difficult problem in mathematics and computer science that was first described by Kautz in the late 1950's (Kautz 1958). Snake-in-the-box codes have many applications in electrical engineering, coding theory, and computer network topologies. Generally, the longer the snake for a given dimension, the more useful it is in… (More)

In this paper we present a genetic algorithm-based optimization technique for edge detection. The problem of edge detection is formulated as one of choosing a minimum cost edge configuration. The edge configurations are viewed as two-dimensional chromosomes with fitness values inversely proportional to their costs. The design of the crossover and the… (More)

We present a method for searching for achordal open paths (snakes) in n-dimensional hypercube graphs (the box). Our technique first obtains a set of exemplary snakes using an evolutionary algorithm. These snakes are then analyzed to define a pruning model that constrains the search space. A depth-first search of the constrained solution space has… (More)

The snake-in-the-box problem is a difficult problem in mathematics and computer science that deals with finding the longest-possible constrained path that can be formed by following the edges of a multi-dimensional hypercube. This problem was first described by Kautz in the late 1950's (Kautz 1958). Snake-in-the-box codes, or`snakes,' are the node or… (More)