Geometric semantic genetic programming for recursive boolean programs
Association for Computing Machinery (ACM)
K. Krawiec acknowledges support from grant 2014/15/B/ST6/05205 funded by the National Science Centre, Poland
Reason for embargo
Embargoed until after conference
Geometric Semantic Genetic Programming (GSGP) induces a unimodal fitness landscape for any problem that consists in finding a function fitting given input/output examples. Most of the work around GSGP to date has focused on real-world applications and on improving the originally proposed search operators, rather than on broadening its theoretical framework to new domains. We extend GSGP to recursive programs, a notoriously challenging domain with highly discontinuous fitness landscapes. We focus on programs that map variable-length Boolean lists to Boolean values, and design search operators that are provably efficient in the training phase and attain perfect generalization. Computational experiments complement the theory and demonstrate the superiority of the new operators to the conventional ones. This work provides new insights into the relations between program syntax and semantics, search operators and fitness landscapes, also for more general recursive domains.
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This is the author accepted manuscript. The final version is available from ACM via the DOI in this record.
GECCO 2017: Genetic and Evolutionary Computation Conference, 15-19 July 2017, Berlin, Germany