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C. FERREIRA Complex Systems, 13 (2): 87-129, 2001

Gene Expression Programming: A New Adaptive Algorithm for Solving Problems

Boolean Concept Learning
The GP rule and the 11-multiplexer are, respectively, boolean functions of seven and 11 activities. Whereas the solution for the 11-multiplexer is a well-known boolean function, the solution of the GP rule is practically unknown, as the program evolved by GP [16] is so complicated that it is impossible to know what the program really does.

This section shows how GEP can be efficiently applied to evolve boolean expressions of several arguments. Furthermore, the structural organization of the chromosomes used to evolve solutions for the 11-multiplexer is an example of a very simple organization that can be used to efficiently solve certain problems. For example, this organization (one-element genes linked by IF) was successfully used to evolve CA rules for the density-classification problem, discovering better rules than the GKL rule (results not shown).

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