Paper Info
Title
Offspring Selection Genetic Algorithm Revisited: Improvements In Efficiency By Early Stopping Criteria In The Evaluation Of Unsuccessful Individuals
Abstract
This paper proposes some algorithmic extensions to the general concept of offspring selection which itself is an algorithmic extension of genetic algorithms and genetic programming. Offspring selection is characterized by the fact that many offspring solution candidates will not participate in the ongoing evolutionary process if they do not achieve the success criterion. The algorithmic enhancements proposed in this contribution aim to early estimate if a solution candidate will not be accepted based on partial solution evaluation. The qualitative characteristics of offspring selection are not affected by this means. The discussed variant of offspring selection is analyzed for several symbolic regression problems with offspring selection genetic programming. The achievable gains in terms of efficiency are remarkable especially for large data-sets.
Year
DOI
Venue
2017
10.1007/978-3-319-74718-7_51
COMPUTER AIDED SYSTEMS THEORY - EUROCAST 2017, PT I
Field
DocType
Volume
Early stopping,Computer science,Offspring,Genetic programming,Artificial intelligence,Symbolic regression,Genetic algorithm,Machine learning
Conference
10671
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
3
6
Name
Order
Citations
PageRank
Michael Affenzeller133962.47
Bogdan Burlacu2214.85
Stephan M. Winkler314022.90
Michael Kommenda49715.58
Gabriel Kronberger519225.40
Stefan Wagner617227.06