1 research outputs found
Competitive Coevolution as an Adversarial Approach to Dynamic Optimization
Dynamic optimization, for which the objective functions change over time, has
attracted intensive investigations due to the inherent uncertainty associated
with many real-world problems. For its robustness with respect to noise,
Evolutionary Algorithms (EAs) have been expected to have great potential for
dynamic optimization. On the other hand, EAs are also criticized for its high
computational complexity, which appears to be contradictory to the core
requirement of real-world dynamic optimization, i.e., fast adaptation
(typically in terms of wall-clock time) to the environmental change. So far,
whether EAs would indeed lead to a truly effective approach for real-world
dynamic optimization remain unclear. In this paper, a new framework of
employing EAs in the context of dynamic optimization is explored. We suggest
that, instead of online evolving (searching) solutions for the ever-changing
objective function, EAs are more suitable for acquiring an archive of solutions
in an offline way, which could be adopted to construct a system to provide
high-quality solutions efficiently in a dynamic environment. To be specific, we
first re-formulate dynamic optimization problems as static set-oriented
optimization problems. Then, a particular type of EAs, namely competitive
coevolution, is employed to search for the archive of solutions in an
adversarial way. The general framework is instantiated for continuous dynamic
constrained optimization problems, and the empirical results showed the
potential of the proposed framework