Performance optimization of multi-core grammatical evolution generated parallel recursive programs

Although Evolutionary Computation (EC) has been used with considerable success to evolve computer programs, the majority of this work has targeted the production of serial code. Recent work with Grammatical Evolution (GE) produced Multi-core Grammatical Evolution (MCGE-II), a system that natively produces parallel code, including the ability to execute recursive calls in parallel. This paper extends this work by including practical constraints into the grammars and fitness functions, such as increased control over the level of parallelism for each individual. These changes execute the best-of-generation programs faster than the original MCGE-II with an average factor of 8.13 across a selection of hard problems from the literature. We analyze the time complexity of these programs and identify avoiding excessive parallelism as a key for further performance scaling. We amend the grammars to evolve a mix of serial and parallel code, which spawns only as many threads as is efficient given the underlying OS and hardware; this speeds up execution by a factor of 9.97.