  Machine Learning (ML) is becoming increasingly important in daily life. In
this context, Artificial Neural Networks (ANNs) are a popular approach within
ML methods to realize an artificial intelligence. Usually, the topology of ANNs
is predetermined. However, there are problems where it is difficult to find a
suitable topology. Therefore, Topology and Weight Evolving Artificial Neural
Network (TWEANN) algorithms have been developed that can find ANN topologies
and weights using genetic algorithms. A well-known downside for large-scale
problems is that TWEANN algorithms often evolve inefficient ANNs and require
long runtimes.
  To address this issue, we propose a new TWEANN algorithm called Artificial
Life Form (ALF) with the following technical advancements: (1) speciation via
structural and semantic similarity to form better candidate solutions, (2)
dynamic adaptation of the observed candidate solutions for better convergence
properties, and (3) integration of solution quality into genetic reproduction
to increase the probability of optimization success. Experiments on large-scale
ML problems confirm that these approaches allow the fast solving of these
problems and lead to efficient evolved ANNs.
