  Genetic Algorithm (GA) -- motivated by natural evolution -- is a robust
method to estimate the global optimal solutions of problems involving multiple
objective functions. In this article, for the first time, we apply GA to
reconstruct the CMB temperature anisotropy map over large angular scales of the
sky using (internal) linear combination (ILC) of the final-year WMAP and Planck
satellite observations. To avoid getting trapped into a local minimum, we
implement the GA with generous diversity in the populations by selecting pairs
with diverse fitness coefficients and by introducing a small but significant
amount of mutation of genes. We find that the new GA-ILC method produces a
clean map which agrees very well with that obtained using the exact analytical
expression of weights in ILC. 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 tend to occupy localized regions
along the 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 also minimal 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.
