  In this paper, we have analyzed the performance-complexity tradeoff of {a
selective} likelihood ascent search (LAS) algorithm initialized by a linear
detector, such as matched filtering (MF), zero forcing (ZF) and minimum mean
square error (MMSE), {and considering an optimization factor $\rho$ from the
bit flipping rule}. The scenario is the uplink of a massive MIMO (M-MIMO)
system, and the {analysis has }been developed by means of computer simulations.
With the increasing number of base station (BS) antennas, the classical
detectors become inefficient. Therefore, the LAS is employed for
performance-complexity tradeoff improvement. Using an adjustable optimized
threshold on the bit flip rule of LAS, much better solutions have been achieved
in terms of BER with no further complexity increment, indicating that there is
an optimal threshold for each scenario. Considering a $32\times32$ antennas
scenario, {the large-scale MIMO system equipped with the proposed LAS detector
with} factor $\rho$ = 0.8 {requires} 5 dB less {in terms of SNR than the
conventional LAS of the} literature ($\rho$ = 1.0) to achieve the same bit
error rate of $10^{-3}$.
