This paper compares three common evolutionary algorithms and our modified GA, a Distributed Adaptive Genetic Algorithm(DAGA). The optimal approach is sought to adapt, in near realtime, biological model behaviour to that of real biology within a laboratory. Near real-time adaptation is achieved with a Graphics Processing Unit (GPU). This, together with evolutionary computation, enables new forms of experimentation such as online testing, where biology and computational model are simultaneously stimulated and their responses compared. Rapid analysis and validation provide a platform that is required for rapid prototyping, and along with online testing, can provide new insight into the cause of biological behaviour. In this context, results demonstrate that our DAGA implementation is more efficient than the other three evolutionary algorithms due to its suitability to the adaptation environment, namely the large population sizes promoted by the GPU architecture.
|Title of host publication||Proceedings of the 10th annual conference on genetic and evolutionary computation|
|Number of pages||78|
|Publication status||Published - 2008|
- Evolutionary Strategies
- Modelling behaviours and ecosystems
- Parameter tuning
- Speedup technique