An Evolutionary Algorithm for Integer Programming

The mutation distribution of evolutionary algorithms usually is oriented at the type of the search space. Typical examples are binomial distributions for binary strings in genetic algorithms or normal distributions for real valued vectors in evolution strategies and evolutionary programming. This paper is devoted to the construction of a mutation distribution for unbounded integer search spaces. The principle of maximum entropy is used to select a specific distribution from numerous potential candidates. The resulting evolutionary algorithm is tested for five nonlinear integer problems.

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