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One of the fundamental problems in coding theory is to determine, for given set of parameters q, n and d, the value Aq (n, d), which is the maximum possible number of code words in a q-ary code of length n and minimum distance d. Codes that attain the maximum are said to be optimal. Being unknown for certain set of parameters, scientists have determined lower bounds, and researchers investigated the use of different evolutionary algorithms for improving lower bounds for a given set of parameters. In this paper, we are interested in finding the set of maximally distant codes for a certain set of parameters, to provide for error detection and/or correction features. For a practically sized problem, it forms a challenge due to the prohibitively of large solution space.

In this work we aim to finding optimal codewords by mapping from the maximal code discovery towards error correcting codes allocation, to the settings of Chemical Reaction Optimization (CRO), a recently developed evolutionary optimization technique, in order to propagate from an initial random state to a final minimal energy state that represents an optimal solution.