Co-evolving functions in genetic programming: Dynamic ADF creation using GliB
In this paper we introduce a novel approach to the use of automatically defined functions (ADFs) with the help of a genetic library builder (GLiB). The new technique uses the two mutation operators of GLiB to automatically create subpopulations of ADFs during evolution, where these are termed evolution-defined functions (EDFs). Our approach consists of dynamically specifying separate subpopulations for each identified ADF, where a further population of programs uses individuals from these subpopulations during evaluations. Using a multiplexer problem and two classification tasks we compare a number of existing methods with this co-evolutionary approach. It is shown that dynamically creating ADF subpopulations (according to worth) proves more beneficial than specifying them a priori. It is also shown that the approach performs better than existing approaches — GP with ADFs and GP with GLiB — at all three tasks. Further, we extend the approach to allow the number of EDFs to emerge during the course of evolution, removing the need to specify how many functions are available a priori.
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