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Self-adaptation of mutation distribution in evolutionary algorithms
This paper is posted here with permission from IEEE - Copyright @ 2007 IEEEThis paper proposes a self-adaptation method to control not only the mutation strength parameter, but also the mutation distribution for evolutionary algorithms. For this purpose, the isotropic g-Gaussian distribution is employed in the mutation operator. The g-Gaussian distribution allows to control the shape of the distribution by setting a real parameter g and can reproduce either finite second moment distributions or infinite second moment distributions. In the proposed method, the real parameter q of the g-Gaussian distribution is encoded in the chromosome of an individual and is allowed to evolve. An evolutionary programming algorithm with the proposed idea is presented. Experiments were carried out to study the performance of the proposed algorithm
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Searching for improvement
Engineering design can be thought of as a search for the best solutions to engineering problems. To perform an effective search, one must distinguish between competing designs and establish a measure of design quality, or fitness. To compare different designs, their features must be adequately described in a well-defined framework, which can mean separating the creative and analytical parts of the design process. By this we mean that a distinction is drawn between coming up with novel design concepts, or architectures, and the process of detailing or refining existing design architecture. In the case of a given design architecture, one can consider the set of all possible designs that could be created by varying its features. If it were possible to measure the fitness of all designs in this set, then one could identify a fitness landscape and search for the best possible solution for this design architecture. In this Chapter, the significance of the interactions between design features in defining the metaphorical fitness landscape is described. This highlights that the efficiency of a search algorithm is inextricably linked to the problem structure (and hence the landscape). Two approaches, namely, Genetic Algorithms (GA) and Robust Engineering Design (RED) are considered in some detail with reference to a case study on improving the design of cardiovascular stents
Evolution in random fitness landscapes: the infinite sites model
We consider the evolution of an asexually reproducing population in an
uncorrelated random fitness landscape in the limit of infinite genome size,
which implies that each mutation generates a new fitness value drawn from a
probability distribution . This is the finite population version of
Kingman's house of cards model [J.F.C. Kingman, \textit{J. Appl. Probab.}
\textbf{15}, 1 (1978)]. In contrast to Kingman's work, the focus here is on
unbounded distributions which lead to an indefinite growth of the
population fitness. The model is solved analytically in the limit of infinite
population size and simulated numerically for finite . When
the genome-wide mutation probability is small, the long time behavior of
the model reduces to a point process of fixation events, which is referred to
as a \textit{diluted record process} (DRP). The DRP is similar to the standard
record process except that a new record candidate (a number that exceeds all
previous entries in the sequence) is accepted only with a certain probability
that depends on the values of the current record and the candidate. We develop
a systematic analytic approximation scheme for the DRP. At finite the
fitness frequency distribution of the population decomposes into a stationary
part due to mutations and a traveling wave component due to selection, which is
shown to imply a reduction of the mean fitness by a factor of compared to
the limit.Comment: Dedicated to Thomas Nattermann on the occasion of his 60th birthday.
Submitted to JSTAT. Error in Section 3.2 was correcte
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