TY - JOUR
T1 - Current Developments and Future Directions of Bio-inspired Computation and Implications for Ecoinformatics
AU - Yao, Xin
AU - Liu, Ying
AU - Li, Jin
AU - He, Jun
AU - Frayn, Colin
PY - 2006/1/1
Y1 - 2006/1/1
N2 - Evolutionary and neural computation has been used widely in solving various problems in biological ecosystems. This paper reviews some of the recent work in evolutionary computation and neural network ensembles that could be explored further in the context of ecoinformatics. Although these bio-inspired techniques were not developed specifically for ecoinformatics, their successes in solving complex problems in other fields demonstrate how these techniques could be adapted and used for tackling difficult problems in ecoinformatics. Firstly, we will review our work in modelling and model calibration, which is an important topic in ecoinformatics. Secondly one example will be given to illustrate how coevolutionary algorithms could be used in problem-solving. Thirdly, we will describe our work on neural network ensembles, which can be used for various classification and prediction problems in ecoinformatics. Finally, we will discuss ecosystem-inspired computational models and algorithms that could be explored as directions of future research. (c) 2005 Elsevier B.V. All rights reserved.
AB - Evolutionary and neural computation has been used widely in solving various problems in biological ecosystems. This paper reviews some of the recent work in evolutionary computation and neural network ensembles that could be explored further in the context of ecoinformatics. Although these bio-inspired techniques were not developed specifically for ecoinformatics, their successes in solving complex problems in other fields demonstrate how these techniques could be adapted and used for tackling difficult problems in ecoinformatics. Firstly, we will review our work in modelling and model calibration, which is an important topic in ecoinformatics. Secondly one example will be given to illustrate how coevolutionary algorithms could be used in problem-solving. Thirdly, we will describe our work on neural network ensembles, which can be used for various classification and prediction problems in ecoinformatics. Finally, we will discuss ecosystem-inspired computational models and algorithms that could be explored as directions of future research. (c) 2005 Elsevier B.V. All rights reserved.
UR - http://www.scopus.com/inward/record.url?scp=31444441106&partnerID=8YFLogxK
U2 - 10.1016/j.ecoinf.2005.07.001
DO - 10.1016/j.ecoinf.2005.07.001
M3 - Article
SN - 1574-9541
VL - 1
SP - 9
EP - 22
JO - Ecological Informatics
JF - Ecological Informatics
IS - 1
ER -