  In poultry farming, the systematic choice, update, and implementation of
periodic (t) action plans define the feed conversion rate (FCR[t]), which is an
acceptable measure for successful production. Appropriate action plans provide
tailored resources for broilers, allowing them to grow within the so-called
thermal comfort zone, without wast or lack of resources. Although the
implementation of an action plan is automatic, its configuration depends on the
knowledge of the specialist, tending to be inefficient and error-prone, besides
to result in different FCR[t] for each poultry house. In this article, we claim
that the specialist's perception can be reproduced, to some extent, by
computational intelligence. By combining deep learning and genetic algorithm
techniques, we show how action plans can adapt their performance over the time,
based on previous well succeeded plans. We also implement a distributed network
infrastructure that allows to replicate our method over distributed poultry
houses, for their smart, interconnected, and adaptive control. A supervision
system is provided as interface to users. Experiments conducted over real data
show that our method improves 5% on the performance of the most productive
specialist, staying very close to the optimal FCR[t].
