Genetic algorithm design inspired by organizational theory: pilot study of a dependency structure matrix driven genetic algorithm

Authors:
Tian-Li Yu;David E. Goldberg;Ali Yassine;Ying-Ping Chen
Affiliations:
University of Illinois at Urbana-Champaign, Urbana, IL;University of Illinois at Urbana-Champaign, Urbana, IL;University of Illinois at Urbana-Champaign, Urbana, IL;University of Illinois at Urbana-Champaign, Urbana, IL
Venue:
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Year:
2003

Citing 0
Cited 6

Nonlinearity linkage detection for financial time series analysis

Proceedings of the 9th annual conference on Genetic and evolutionary computation
Graph clustering based model building

PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Hierarchical allelic pairwise independent functions

Proceedings of the 13th annual conference on Genetic and evolutionary computation
Transformations in the scale of behavior and the global optimization of constraints in adaptive networks

Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
A Markovianity based optimisation algorithm

Genetic Programming and Evolvable Machines
Linkage learning by number of function evaluations estimation: Practical view of building blocks

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

This study proposes a dependency structure matrix driven genetic algorithm (DSMDGA) which utilizes the dependency structure matrix (DSM) clustering to extract building block (BB) information and use the information to accomplish BB-wise crossover. Three cases: tight, loose, and random linkage, are tested on both a DSMDGA and a simple genetic algorithm (SGA). Experiments showed that the DSMDGA is able to correctly identify BBs and outperforms a SGA.