  When controllers (brains) and morphologies (bodies) of robots simultaneously
evolve, this can lead to a problem, namely the brain & body mismatch problem.
In this research, we propose a solution of lifetime learning. We set up a
system where modular robots can create offspring that inherit the bodies of
parents by recombination and mutation. With regards to the brains of the
offspring, we use two methods to create them. The first one entails solely
evolution which means the brain of a robot child is inherited from its parents.
The second approach is evolution plus learning which means the brain of a child
is inherited as well, but additionally is developed by a learning algorithm -
RevDEknn. We compare these two methods by running experiments in a simulator
called Revolve and use efficiency, efficacy, and the morphology intelligence of
the robots for the comparison. The experiments show that the evolution plus
learning method does not only lead to a higher fitness level, but also to more
morphologically evolving robots. This constitutes a quantitative demonstration
that changes in the brain can induce changes in the body, leading to the
concept of morphological intelligence, which is quantified by the learning
delta, meaning the ability of a morphology to facilitate learning.
