Abstract In last 30 years, effectiveness of ES algorithms has induced many people to believe that they are the methods of choice for hard real-life problems superseding traditional search techniques. However, they are not without their limitations. In particular, the choice of a good evolutionary operator can make a considerable difference to the exploration and exploitation, and often even the feasibility of the evolutionary search. Moreover, the success and progress of an evolutionary search algorithm mostly depends upon the balance between population diversity and selective pressure [97]. To meet these requirements, contemporary ESs [11, 15, 110, 124, 16, 17] usually apply self-adaptation in so-called strategy parameters (or internal model) of the object variables. While optimizing the objective function, the self-adaptation technique requires optimization of the strategy parameters as well [108]. This dual optimization creates algorithmic complexity and in turn, it causes expense in computation time. Regarding this, Bäck et al. [17][p.7] stated as
The amount of information included into the individuals by means of the self-adaptation principle increases from the simple case of one standard deviation up to the order of n2 additional parameters, which reflects an enormous degree offreedom for the internal models of the individuals. This growing degree offreedom often enhances the global search capabilities of the algorithm at the cost of the expense in computation time, and...
A Novel Evolution Strategy Algorithm
01.01.2004
32 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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