  Evolution is the theory that plants and animals today have come from kinds
that have existed in the past. Scientists such as Charles Darwin and Alfred
Wallace dedicate their life to observe how species interact with their
environment, grow, and change. We are able to predict future changes as well as
simulate the process using genetic algorithms. Genetic Algorithms give us the
opportunity to present multiple variables and parameters to an environment and
change values to simulate different situations. By optimizing genetic
algorithms to hold entities in an environment, we are able to assign varying
characteristics such as speed, size, and cloning probability, to the entities
to simulate real natural selection and evolution in a shorter period of time.
Learning about how species grow and evolve allows us to find ways to improve
technology, help animals going extinct to survive, and figure* out how diseases
spread and possible ways of making an environment uninhabitable for them. Using
data from an environment including genetic algorithms and parameters of speed,
size, and cloning percentage, the ability to test several changes in the
environment and observe how the species interacts within it appears. After
testing different environments with a varied amount of food while keeping the
number of starting population at 10 entities, it was found that an environment
with a scarce amount of food was not sustainable for small and slow entities.
All environments displayed an increase in speed, but the environments that were
richer in food allowed for the entities to live for the entire duration of 50
generations, as well as allowed the population to grow significantly.
