This contribution provides functions for finding an optimum parameter set using the EVOLUTIONary algorithm of Differential Evolution. Simply speaking: If you have some complicated function of which you are unable to compute a derivative, and you want to find the parameter set minimizing the output of the function, using this package is one possible way to go.
EVOLUTIONary Computation (EC) deals with problem solving, optimization, and
machine learning techniques inspired by principles of natural evolution and ge-
netics. Just from this basic definition, it is clear that one of the main features of
the research community involved in the study of its theory and in its applications
is multidisciplinarity. For this reason, EC has been able to draw the attention of
an ever-increasing number of researchers and practitioners in several fields.
- XCS for Dynamic Environments
+ Continuous versions of XCS
+ Test problem: real multiplexer
+ Experiments: XCS is explored in dynamic environments with different magnitudes of change to the underlying concepts.
+Reference papers:
H.H. Dam, H.A. Abbass, C.J. Lokan, EVOLUTIONary Online Data Mining – an Investigation in a Dynamic Environment. 2005, accepted for a book chapter in Springer Series on Studies in Computational Intelligence
H.H. Dam, H.A. Abbass, C.J. Lokan, Be Real! XCS with Continuous-Valued Inputs. IWLCS 2005, (International Workshop on Learning Classifier Systems). Washington DC, June 2005.
采用蟻群算法檢測圖像邊緣
This a demo program of image edge detection using ant colony, based on the paper, "An Ant Colony Optimization Algorithm For Image Edge Detection," IEEE Congress on EVOLUTIONary Computation (CEC), pp. 751-756, Hongkong, Jun. 2008.
If you are acquainted with neural networks, automatic control problems
are good industrial applications and have a dynamic or EVOLUTIONary nature
lacking in static pattern-recognition; control ideas are also prevalent in the
study of the natural neural networks found in animals and human beings.
If you are interested in the practice and theory of control, artificial neu-
ral networks offer a way to synthesize nonlinear controllers, filters, state
observers and system identifiers using a parallel method of computation.
