TY - GEN
T1 - Self-adaptive population size adjustment for genetic algorithms
AU - Affenzeller, Michael
AU - Wagner, Stefan
AU - Winkler, Stephan
N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2007
Y1 - 2007
N2 - Variable population sizing techniques are rarely considered in the theory of Genetic Algorithms. This paper discusses a new variant of adaptive population sizing for this class of Evolutionary Algorithms. The basic idea is to adapt the actual population size depending on the actual ease or difficulty of the algorithm in its ultimate goal to generate new child chromosomes that outperform their parents.
AB - Variable population sizing techniques are rarely considered in the theory of Genetic Algorithms. This paper discusses a new variant of adaptive population sizing for this class of Evolutionary Algorithms. The basic idea is to adapt the actual population size depending on the actual ease or difficulty of the algorithm in its ultimate goal to generate new child chromosomes that outperform their parents.
UR - http://www.scopus.com/inward/record.url?scp=38449090772&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-75867-9_103
DO - 10.1007/978-3-540-75867-9_103
M3 - Conference contribution
SN - 9783540758662
VL - 4739
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 820
EP - 828
BT - Computer Aided Systems Theory - EUROCAST 2007 - 11th International Conference on Computer Aided Systems Theory, Revised Selected Papers
PB - Springer
T2 - 11th International Conference on Computer Aided Systems Theory, EUROCAST 2007
Y2 - 12 February 2007 through 16 February 2007
ER -