  Tropical forests represent the home of many species on the planet for flora
and fauna, retaining billions of tons of carbon footprint, promoting clouds and
rain formation, implying a crucial role in the global ecosystem, besides
representing the home to countless indigenous peoples. Unfortunately, millions
of hectares of tropical forests are lost every year due to deforestation or
degradation. To mitigate that fact, monitoring and deforestation detection
programs are in use, in addition to public policies for the prevention and
punishment of criminals. These monitoring/detection programs generally use
remote sensing images, image processing techniques, machine learning methods,
and expert photointerpretation to analyze, identify and quantify possible
changes in forest cover. Several projects have proposed different computational
approaches, tools, and models to efficiently identify recent deforestation
areas, improving deforestation monitoring programs in tropical forests. In this
sense, this paper proposes the use of pattern classifiers based on
neuroevolution technique (NEAT) in tropical forest deforestation detection
tasks. Furthermore, a novel framework called e-NEAT has been created and
achieved classification results above $90\%$ for balanced accuracy measure in
the target application using an extremely reduced and limited training set for
learning the classification models. These results represent a relative gain of
$6.2\%$ over the best baseline ensemble method compared in this paper
