  Genetic Algorithm (GA) -- motivated by the natural evolutionary process -- is
a robust method to estimate the optimal solutions of problems involving one or
more objective functions. In this article, for the first time, we apply GA to
reconstruct the cleaned CMB temperature anisotropy map over large angular
scales of the sky using (internal) linear combination (ILC) of observations
from the final-year WMAP and Planck satellite missions. To avoid getting
trapped into a local minimum, we implement the GA with generous diversity in
the populations. This is achieved by introducing a small but significant amount
of mutation of genes during crossover and selecting pairs with diverse fitness
coefficients. We find that the new GA-ILC method produces a cleaned CMB map
which agrees very well with the CMB map obtained using the exact and analytical
expression of weights in the ILC method. By performing extensive Monte Carlo
simulations of the CMB reconstruction using the GA-ILC algorithm we find that
residual foregrounds in the cleaned map are minimal and mostly tend to occupy
localized regions along the central galactic plane. The CMB angular power
spectrum shows no indication of any bias in the entire multipole range $2 \leq
\ell \leq 32$ studied in this work. The error in the CMB angular power spectrum
is minimal as well and given entirely by the cosmic variance induced error. Our
results agree well with those obtained by various other reconstruction methods
by different research groups. This problem-independent robust GA-ILC method
provides a flexible way towards the complex and challenging task of CMB
component reconstruction in cosmology.
