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Efficient tree traversal to reduce code growth in tree-based genetic programming

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Abstract

Genetic programming is an evolutionary optimization method following the principle of program induction. Genetic programming often uses variable-length tree structures for representing candidate solutions. A serious problem with variable-length representations is code growth: during evolution these tree structures tend to grow in size without a corresponding increase in fitness. Many anti-bloat methods focus solely on size reduction and forget about fitness improvement, which is rather strange when using an “optimization” method. This paper reports the application of a semantically driven local search operator to control code growth and improve best fitness. Five examples, two theoretical benchmark applications and three real-life test problems are used to illustrate the obtained size reduction and fitness improvement. Performance of the local search operator is also compared with various other anti-bloat methods such as size and depth delimiters, an expression simplifier, linear and adaptive parsimony pressure, automatically defined functions and Tarpeian bloat control.

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  1. Department of Electrical Energy, Systems & Automation, Ghent University, Technologiepark 913, 9052, Zwijnaarde, Belgium

    Bart Wyns & Luc Boullart

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  1. Bart Wyns
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  2. Luc Boullart
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Correspondence to Bart Wyns.

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Wyns, B., Boullart, L. Efficient tree traversal to reduce code growth in tree-based genetic programming. J Heuristics 15, 77–104 (2009). https://doi.org/10.1007/s10732-007-9060-0

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