Title :
Sunspot prediction using genetic programming augmented by Binary String Fitness Characterisation and Comparative Partner Selection
Author :
Day, Peter ; Nandi, Asoke K.
Author_Institution :
Dept. of Electr. Eng. & Electron., Univ. of Liverpool, Liverpool
Abstract :
The paper addresses the sunspot prediction problem utilising a novel strategy for evaluating individualpsilas relative strengths and weaknesses, by representing these in the form of a binary string fitness characterisation (BSFC), in addition to an overall fitness value for each individual. Utilising a combination of the BSFC and a pair-wise mating strategy, comparative partner selection (CPS), appears to promote effective solutions by reducing population-wide weaknesses. This strategy offers better solution to the sunspot prediction problem.
Keywords :
genetic algorithms; prediction theory; string matching; sunspots; binary string fitness characterisation; comparative partner selection; genetic programming; pair-wise mating strategy; population-wide weaknesses; sunspot prediction; Character generation; Computational efficiency; Genetic programming; Optimization methods; Power generation; Binary String Fitness Characterisation; Comparative Partner Selection; Diversity; Genetic Programming; Sunspot Prediction;
Conference_Titel :
Machine Learning for Signal Processing, 2008. MLSP 2008. IEEE Workshop on
Conference_Location :
Cancun
Print_ISBN :
978-1-4244-2375-0
Electronic_ISBN :
1551-2541
DOI :
10.1109/MLSP.2008.4685475