Paper Info
Title
Diversity control in GP with ADF for regression tasks
Abstract
This paper proposes a two-phase diversity control approach to prevent the common problem of the loss of diversity in Genetic Programming with Automatically Defined Functions. While most recent work focuses on diagnosing and remedying the loss of diversity, this approach aims to prevent the loss of diversity in the early stage through a refined diversity control method and a fully covered tournament selection method. The results on regression tasks suggest that these methods can effectively improve the system performance by reducing the incidences of premature convergence and the number of generations needed for finding an optimal solution.
Year
DOI
Venue
2005
10.1007/11589990_181
Australian Conference on Artificial Intelligence
Keywords
Field
DocType
genetic programming,premature convergence,optimal solution,regression task,common problem,recent work,early stage,two-phase diversity control approach,refined diversity control method,automatically defined functions,tournament selection method,system performance
Tournament,Regression,Premature convergence,Computer science,Genetic programming,Automatically defined functions,Artificial intelligence,Tournament selection,Genetic algorithm
Conference
Volume
ISSN
ISBN
3809
0302-9743
3-540-30462-2
Citations 
PageRank 
References 
2
0.36
2
Authors
1
Name
Order
Citations
PageRank
Huayang Xie1859.96