Semantic methods in genetic programming

Abstract

We are delighted to present this special issue of Genetic Programming and Evolvable Machines on Semantic Methods in Genetic Programming. Genetic programming (GP)—the application of evolutionary computing techniques to the creation of computer programs—has been a key topic in computational intelligence in the last couple of decades. In recent years an emerging topic in GP has been the use of semantic methods. The aim of this Special Issue is to provide a way of exploring the input–output behaviour of programs, which is ultimately what matters for problem solving. This contrasts with much previous work in GP, which has a syntactical focus, where operators transform the program text and the effect on program behaviour is indirect. The use of semantic methods in GP has substantially improved the performance of GP on a number of problems, including both benchmarks and real-world applications. Semantic methods in GP have also been grounded in a body of theory, which helps to inform algorithm design. Following an open call for papers, this issue is comprised of three articles with a common thread of geometric semantic operators, a very popular and fruitful direction of research within the community, which exploit geometric properties of program semantics. The special issue was preceded by two international workshops on Semantic Methods in Genetic Programming, the first held at PPSN (13–17 September 2014 in Ljubljana, Slovenia) the 13th International Conference on Parallel Problem Solving from Nature, and the second at the ACM Genetic and Evolutionary Computation Conference GECCO 2015 (11–15 July 2015 in Madrid, Spain). The three articles