  In this paper, we evolve a card-choice strategy for the arena mode of Legends
of Code and Magic, a programming game inspired by popular collectible card
games like Hearthstone or TES: Legends. In the arena game mode, before each
match, a player has to construct his deck choosing cards one by one from the
previously unknown options. Such a scenario is difficult from the optimization
point of view, as not only the fitness function is non-deterministic, but its
value, even for a given problem instance, is impossible to be calculated
directly and can only be estimated with simulation-based approaches. We propose
a variant of the evolutionary algorithm that uses a concept of an active gene
to reduce the range of the operators only to generation-specific subsequences
of the genotype. Thus, we batched learning process and constrained evolutionary
updates only to the cards relevant for the particular draft, without forgetting
the knowledge from the previous tests. We developed and tested various
implementations of this idea, investigating their performance by taking into
account the computational cost of each variant. Performed experiments show that
some of the introduced active-genes algorithms tend to learn faster and produce
statistically better draft policies than the compared methods.
