Evolving Real-Time Heuristics Search Algorithms with Building Blocks
  The research area of real-time heuristics search has produced quite many
algorithms. In the landscape of real-time heuristics search research, it is not
rare to find that an algorithm X that appears to perform better than algorithm
Y on a group of problems, performed worse than Y for another group of problems.
If these published algorithms are combined to generate a more powerful space of
algorithms, then that novel space of algorithms may solve a distribution of
problems more efficiently. Based on this intuition, a recent work Bulitko 2016
has defined the task of finding a combination of heuristics search algorithms
as a survival task. In this evolutionary approach, a space of algorithms is
defined over a set of building blocks published algorithms and a simulated
evolution is used to recombine these building blocks to find out the best
algorithm from that space of algorithms.
  In this paper, we extend the set of building blocks by adding one published
algorithm, namely lookahead based A-star shaped local search space generation
method from LSSLRTA-star, plus an unpublished novel strategy to generate local
search space with Greedy Best First Search. Then we perform experiments in the
new space of algorithms, which show that the best algorithms selected by the
evolutionary process have the following property: the deeper is the lookahead
depth of an algorithm, the lower is its suboptimality and scrubbing complexity.
