The use of niching methods for solving real world optimization problems is limited by the difficulty to obtain a proper setting of the speciation parameters without any a priori information about the fitness landscape. To avoid such a difficulty, we propose a novel method, called Adaptive Species Discovery, that removes the basic assumption of perfect discrimination among peaks underlying Fitness Sharing and, consequently, allows to overcome the drawbacks of the most performing sharing-based methods. This is achieved through an explicit mechanism able to discover the species in the population during the evolution. The method does not require any a priori knowledge, in that it makes no assumption about the location and the shape of the peaks, while it exploits information about the ruggedness of the fitness landscape, dynamically acquired at each generation. The proposed method has been evaluated on a set of standard functions largely adopted in the literature to assess the performance of niching methods. The experimental results show that our method has a better ability to discover and maintain all the peaks with respect to other methods proposed so far.

Speciation in evolutionary algorithms: adaptive species discovery

DELLA CIOPPA, Antonio
;
MARCELLI, Angelo;
2011

Abstract

The use of niching methods for solving real world optimization problems is limited by the difficulty to obtain a proper setting of the speciation parameters without any a priori information about the fitness landscape. To avoid such a difficulty, we propose a novel method, called Adaptive Species Discovery, that removes the basic assumption of perfect discrimination among peaks underlying Fitness Sharing and, consequently, allows to overcome the drawbacks of the most performing sharing-based methods. This is achieved through an explicit mechanism able to discover the species in the population during the evolution. The method does not require any a priori knowledge, in that it makes no assumption about the location and the shape of the peaks, while it exploits information about the ruggedness of the fitness landscape, dynamically acquired at each generation. The proposed method has been evaluated on a set of standard functions largely adopted in the literature to assess the performance of niching methods. The experimental results show that our method has a better ability to discover and maintain all the peaks with respect to other methods proposed so far.
9781450305570
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/3029031
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