  This research project investigates Lenia, an artificial life platform that
simulates ecosystems of digital creatures. Lenia's ecosystem consists of
simple, artificial organisms that can move, consume, grow, and reproduce. The
platform is important as a tool for studying artificial life and evolution, as
it provides a scalable and flexible environment for creating a diverse range of
organisms with varying abilities and behaviors. Measuring complexity in Lenia
is a key aspect of the study, which identifies the metrics for measuring
long-term complex emerging behavior of rules, with the aim of evolving better
Lenia behaviors which are yet not discovered. The Genetic Algorithm uses
neighborhoods or kernels as genotype while keeping the rest of the parameters
of Lenia as fixed, for example growth function, to produce different behaviors
respective to the population and then measures fitness value to decide the
complexity of the resulting behavior. First, we use Variation over Time as a
fitness function where higher variance between the frames are rewarded. Second,
we use Auto-encoder based fitness where variation of the list of reconstruction
loss for the frames is rewarded. Third, we perform combined fitness where
higher variation of the pixel density of reconstructed frames is rewarded. All
three experiments are tweaked with pixel alive threshold and frames used.
Finally, after performing nine experiments of each fitness for 500 generations,
we pick configurations from all experiments such that there is a scope of
further evolution, and run it for 2500 generations. Results show that the
kernel's center of mass increases with a specific set of pixels and together
with borders the kernel try to achieve a Gaussian distribution. Results are
available at https://s4nyam.github.io/evolenia/
