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Solving the Majority Classification Problem
Austin Rachlin
Background:The Majority Classification Problem deals with a test array with binary cells that are randomly turned either on or off at the beginning of the program. The test array has an odd number of cells so that a majority of the cells are either turned on or off. A rule array is created that defines how each cell of the test array should evolve in each step of the cellular automata. The purpose of the rule array is to turn the entire test array either on or off, whichever is the majority in the original test array. The goal of this problem is to create an algorithm that will produce an array that will turn a high percentage of test arrays toward their majorities.
Description:
This specific variation of the problem deals with test arrays that are 149 cells long. Because a 7-neighbor system is used (the specific cell and four cells on either side are observed to determine the resultant cell), the rule arrays are 128 cells long to cover every possible combination of test cells. Wrap-around is used when considering cells close to either end of the test array: if there aren't enough cells on one side, cells from the other end of the array are used. If a rule array fails to correctly evaluate a test array in 1000 steps, that particular run is considered a failure. Lastly, the final and best array is tested on a test space of 1,000,000 test arrays.
Graphics: (Sample Run)
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