José A. García Gutiérrez, Carlos Cotta, Antonio J. Fernández-Leiva
In general Evolutionary Computation (EC) includes a number of optimization
methods inspired by biological mechanisms of evolution. The methods catalogued
in this area use the Darwinian principles of life evolution to produce
algorithms that returns high quality solutions to hard-to-solve optimization
problems. The main strength of EC is precisely that they provide good solutions
even if the computational resources (e.g., running time) are limited. Astronomy
and Astrophysics are two fields that often require optimizing problems of high
complexity or analyzing a huge amount of data and the so-called complete
optimization methods are inherently limited by the size of the problem/data.
For instance, reliable analysis of large amounts of data is central to modern
astrophysics and astronomical sciences in general. EC techniques perform well
where other optimization methods are inherently limited (as complete methods
applied to NP-hard problems), and in the last ten years, numerous proposals
have come up that apply with greater or lesser success methodologies of
evolutional computation to common engineering problems. Some of these problems,
such as the estimation of non-lineal parameters, the development of automatic
learning techniques, the implementation of control systems, or the resolution
of multi-objective optimization problems, have had (and have) a special
repercussion in the fields. For these reasons EC emerges as a feasible
alternative for traditional methods. In this paper, we discuss some promising
applications in this direction and a number of recent works in this area; the
paper also includes a general description of EC to provide a global perspective
to the reader and gives some guidelines of application of EC techniques for
future research
View original:
http://arxiv.org/abs/1202.2523
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