  Mechanized theorem proving is becoming the basis of reliable systems
programming and rigorous mathematics. Despite decades of progress in proof
automation, writing mechanized proofs still requires engineers' expertise and
remains labor intensive. Recently, researchers have extracted heuristics of
interactive proof development from existing large proof corpora using
supervised learning. However, such existing proof corpora present only one way
of proving conjectures, while there are often multiple equivalently effective
ways to prove one conjecture. In this abstract, we identify challenges in
discovering heuristics for automatic proof search and propose our novel
approach to improve heuristics of automatic proof search in Isabelle/HOL using
evolutionary computation.
