In the last two decades, numerous evolutionary algorithms
(EAs) have been developed for solving
optimization problems. However, only a few works have
focused on the question of the termination
criteria. Indeed, EAs still need termination criteria
prespecified by the user. In this paper, we
develop a genetic algorithm (GA) with automatic
termination and acceleration elements which allow the
search to end without resort to predefined conditions.
We call this algorithm "Genetic Algorithm with Automatic
Termination and Search Space Rotation", abbreviated as GATR.
This algorithm utilizes the so-called "Gene Matrix" (GM)
to equip the search process with a self-check in order
to judge how much exploration has been performed,
while maintaining the population diversity. The algorithm also implements
a mutation operator called "mutagenesis" to achieve more efficient and
faster exploration and exploitation processes. Moreover,
GATR fully exploits the structure of the GM by calling
a novel search space decomposition mechanism combined
with a search space rotation procedure. As a result, the search operates
strictly within two-dimensional subspaces irrespective of the dimension of
the original problem. The computational experiments and comparisons with
some state-of-the-art EAs demonstrate the effectiveness of the automatic
termination criteria and the space decomposition mechanism of GATR.