6 research outputs found
A Clustering-Based Model-Building EA for Optimization Problems with Binary and Real-Valued Variables
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
Learning and exploiting mixed variable dependencies with a model-based EA
Mixed-integer optimization considers problems with both discrete and continuous variables. The ability to learn and process problem structure can be of paramount importance for optimization, particularly when faced with black-box optimization (BBO) problems, where no structural knowledge is known a priori. For such cases, model-based Evolutionary Algorithms (EAs) have been very successful in the fields of discrete and continuous optimization. In this paper, we present a model-based EA which integrates techniques from the discrete and continuous domains in order to tackle mixed-integer problems. We furthermore introduce the novel mechanisms to learn and exploit mixed-variable dependencies. Previous approaches only learned dependencies explicitly in either the discrete or the continuous domain. The potential usefulness of addressing mixed dependencies directly is assessed by empirically analyzing algorithm performance on a selection of mixed-integer problems with different types of variable interactions. We find substantially improved, scalable performance on problems that exhibit mixed dependencies
Learning and exploiting mixed variable dependencies with a model-based EA
Mixed-integer optimization considers problems with both discrete and continuous variables. The ability to learn and process problem structure can be of paramount importance for optimization, particularly when faced with black-box optimization (BBO) problems, where no structural knowledge is known a priori. For such cases, model-based Evolutionary Algorithms (EAs) have been very successful in the fields of discrete and continuous optimization. In this paper, we present a model-based EA which integrates techniques from the discrete and continuous domains in order to tackle mixed-integer problems. We furthermore introduce the novel mechanisms to learn and exploit mixed-variable dependencies. Previous approaches only learned dependencies explicitly in either the discrete or the continuous domain. The potential usefulness of addressing mixed dependencies directly is assessed by empirically analyzing algorithm performance on a selection of mixed-integer problems with different types of variable interactions. We find substantially improved, scalable performance on problems that exhibit mixed dependencies
GAMBIT: A parameterless model-based evolutionary algorithm for mixed-integer problems
Learning and exploiting problem structure is one of the key challenges in optimization. This is especially important for black-box optimization (BBO) where prior structural knowledge of a problem is not available. Existing model-based Evolutionary Algorithms (EAs) are very efficient at learning structure in both the discrete, and in the continuous domain. In this article, discrete and continuous model-building mechanisms are integrated for the Mixed-Integer (MI) domain, comprising discrete and continuous variables. We revisit a recently introduced model-based evolutionary algorithm for the MI domain, the Genetic Algorithm for Model-Based mixed-Integer opTimization (GAMBIT). We extend GAMBIT with a parameterless scheme that allows for practical use of the algorithm without the need to explicitly specify any parameters. We furthermore contrast GAMBIT with other model-based alternatives. The ultimate goal of processing mixed dependences explicitly in GAMBIT is also addressed by introducing a new mechanism for the explicit exploitation of mixed dependences. We find that processing mixed dependences with this novel mechanism allows for more efficient optimization. We further contrast the parameterless GAMBIT with Mixed-Integer Evolution Strategies (MIES) and other state-of-the-art MI optimization algorithms from the General Algebraic Modeling System (GAMS) commercial algorithm suite on problems with and without constraints, and show that GAMBIT is capable of solving problems where variable dependences prevent many algorithms from successfully optimizing them
A Clustering-Based Model-Building EA for Optimization Problems with Binary and Real-Valued Variables
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
A Clustering-Based Model-Building EA for Optimization Problems with Binary and Real-Valued Variables
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