Abstract
The use of Genetic Programming (GP) to optimise increasingly large software code has been enabled through biasing the application of GP operators to code areas relevant to the optimisation of interest. As previous approaches have used various forms of static bias applied before the application of GP, we show the emergence of bias learned within the GP process itself which improves solution finding probability in a similar way. As this variant technique is sensitive to the evolutionary lineage, we argue that it may more accurately provide bias in programs which have undergone heavier modification and thus find solutions addressing more complex issues.
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Cody-Kenny, B., Barrett, S. (2013). The Emergence of Useful Bias in Self-focusing Genetic Programming for Software Optimisation. In: Ruhe, G., Zhang, Y. (eds) Search Based Software Engineering. SSBSE 2013. Lecture Notes in Computer Science, vol 8084. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39742-4_29
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DOI: https://doi.org/10.1007/978-3-642-39742-4_29
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