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Genetic Programming for Evolving Computer Programs with Loop Structures for Classification Tasks

Genetic Programming (GP) is an evolutionary technique that evolves computer programs to solve a problem. GP is commonly used for classification tasks where an input must be classified into a single category. The use of standard binary operators has proven effective for classification, but recent research has found that loop structures can generate notably better results. It is still unclear how characteristics of loop structures affect different classification tasks. This research proposes five novel loop structures with different characteristics and analyses their effectiveness and understandability against both image and nonimage classification tasks. Results find that the proposed loop structures can outperform GP with no loops and existing loop structures. Results are also simpler and more human understandable than GP with no loops and existing loop structures.