1 research outputs found
Gray-box optimization and factorized distribution algorithms: where two worlds collide
The concept of gray-box optimization, in juxtaposition to black-box
optimization, revolves about the idea of exploiting the problem structure to
implement more efficient evolutionary algorithms (EAs). Work on factorized
distribution algorithms (FDAs), whose factorizations are directly derived from
the problem structure, has also contributed to show how exploiting the problem
structure produces important gains in the efficiency of EAs. In this paper we
analyze the general question of using problem structure in EAs focusing on
confronting work done in gray-box optimization with related research
accomplished in FDAs. This contrasted analysis helps us to identify, in current
studies on the use problem structure in EAs, two distinct analytical
characterizations of how these algorithms work. Moreover, we claim that these
two characterizations collide and compete at the time of providing a coherent
framework to investigate this type of algorithms. To illustrate this claim, we
present a contrasted analysis of formalisms, questions, and results produced in
FDAs and gray-box optimization. Common underlying principles in the two
approaches, which are usually overlooked, are identified and discussed.
Besides, an extensive review of previous research related to different uses of
the problem structure in EAs is presented. The paper also elaborates on some of
the questions that arise when extending the use of problem structure in EAs,
such as the question of evolvability, high cardinality of the variables and
large definition sets, constrained and multi-objective problems, etc. Finally,
emergent approaches that exploit neural models to capture the problem structure
are covered.Comment: 33 pages, 9 tables, 3 figures. This paper covers some of the topics
of the talk "When the gray box was opened, model-based evolutionary
algorithms were already there" presented in the Model-Based Evolutionary
Algorithms workshop on July 20, 2016, in Denve