2015
A Clustering-Based Model-Building EA for Optimization Problems with Binary and Real-Valued Variables
Publication
Publication
Presented at the
Genetic and Evolutionary Computation Conference
We propose a novel clustering-based model-building evolutionary
algorithm to tackle optimization problems that
have both binary and real-valued variables. The search
space is clustered every generation using a distance metric
that considers binary and real-valued variables jointly
in order to capture and exploit dependencies between variables
of different types. After clustering, linkage learning
takes place within each cluster to capture and exploit dependencies
between variables of the same type. We compare
this with a model-building approach that only considers dependencies
between variables of the same type. Additionally, since many
real-world problems have constraints, we
examine the use of different well-known approaches to handling
constraints: constraint domination, dynamic penalty
and global competitive ranking. We experimentally analyze
the performance of the proposed algorithms on various
unconstrained problems as well as a selection of well-known
MINLP benchmark problems that all have constraints, and
compare our results with the Mixed-Integer Evolution Strategy
(MIES). We find that our approach to clustering that is
aimed at the processing of dependencies between binary and
real-valued variables can significantly improve performance
in terms of required population size and function evaluations
when solving problems that exhibit properties such as multiple
optima, strong mixed dependencies and constraints.
Additional Metadata | |
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, | |
ACM | |
S. Silva , A.I. Esparcia-Alcázar | |
doi.org/10.1145/2739480.2754740 | |
Genetic and Evolutionary Computation Conference | |
Organisation | Intelligent and autonomous systems |
Sadowski, K., Bosman, P., & Thierens, D. (2015). A Clustering-Based Model-Building EA for Optimization Problems with Binary and Real-Valued Variables. In S. Silva & A. I. Esparcia-Alcázar (Eds.), Proceedings of Genetic and Evolutionary Computation Conference 2015. ACM. doi:10.1145/2739480.2754740 |
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inProceedings
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