author = "Feiyu Zhang and Yuning Chen and Yingwu Chen",
booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)",
title = "Evolving Constructive Heuristics for Agile Earth
Observing Satellite Scheduling Problem with Genetic
Programming",
year = "2018",
abstract = "Agile Earth Observing Satellite (AEOS) scheduling
problem (AEOSSP) consists in selecting a subset of
tasks from a given task set which are then scheduled on
the agile satellite with the purpose of maximizing the
total reward of scheduled tasks. AEOSSP is strongly
NP-hard and therefore existing solution approaches
mainly fall in the field of heuristics and
metaheuristics. According to the no free lunch theory,
it is impossible to find a single heuristic that is
well-applied to any problem instance and a
problem-tailored heuristic is always needed. In this
paper, we propose a genetic programming based
evolutionary approach (GPEA) to automatically evolve a
best-suited constructive heuristic for any given AEOSSP
instance. The programs (individuals) of GPEA are
heuristic rules encoded as trees of mathematical
functions. The fitness of the program is evaluated
through mapping the mathematical function to an AEOSSP
solution using a timeline-based construction algorithm.
Computational results on a set of well-designed AEOSSP
scenarios show that the proposed GPEA leads to a
heuristic algorithm that outperforms recently published
sophisticated meta-heuristic algorithm (ALNS).
Additional experiments were carried out to demonstrate
that the time-line based construction algorithm plays a
significant role in matching time-related
characteristics in comparison to four commonly used
heuristic algorithms. Our results also showed that the
evolved heuristic rules preserve a certain extent of
generality.",