  Lexicase selection achieves very good solution quality by introducing ordered
test cases. However, the computational complexity of lexicase selection can
prohibit its use in many applications. In this paper, we introduce Batch
Tournament Selection (BTS), a hybrid of tournament and lexicase selection which
is approximately one order of magnitude faster than lexicase selection while
achieving a competitive quality of solutions. Tests on a number of regression
datasets show that BTS compares well with lexicase selection in terms of mean
absolute error while having a speed-up of up to 25 times. Surprisingly, BTS and
lexicase selection have almost no difference in both diversity and performance.
This reveals that batches and ordered test cases are completely different
mechanisms which share the same general principle fostering the specialization
of individuals. This work introduces an efficient algorithm that sheds light
onto the main principles behind the success of lexicase, potentially opening up
a new range of possibilities for algorithms to come.
