  In this paper, we propose a novel methodology for automatically finding new
chaotic attractors through a computational intelligence technique known as
multi-gene genetic programming (MGGP). We apply this technique to the case of
the Lorenz attractor and evolve several new chaotic attractors based on the
basic Lorenz template. The MGGP algorithm automatically finds new nonlinear
expressions for the different state variables starting from the original Lorenz
system. The Lyapunov exponents of each of the attractors are calculated
numerically based on the time series of the state variables using time delay
embedding techniques. The MGGP algorithm tries to search the functional space
of the attractors by aiming to maximise the largest Lyapunov exponent (LLE) of
the evolved attractors. To demonstrate the potential of the proposed
methodology, we report over one hundred new chaotic attractor structures along
with their parameters, which are evolved from just the Lorenz system alone.
