Abstract



Encoding feasible solutions is one of the most important aspects to be taken into account in the ?eld of evolutionary computation in order to solve search or optimization problems. This paper proposes a new encoding scheme for realcoded evolutionary algorithms. It is called partition based encoding scheme, and satis?es two restrictions. Firstly, each of the components of a decoded vector that conforms a candidate solution to a problem at hand belongs to a prede?ned interval. Secondly, the sum of the components of each of these decoded vectors is always equal to a prede?ned constant. The proposed encoding scheme inherently guarantees these constraints for all the individuals that are generated within the evolution process as a consequence of applying the genetic operators. Partition based encoding scheme is successfully applied to learning conditional probability tables for a given discrete Bayesian network topology, where each row of the tables must exactly add up to one, and the components of each row belong to the interval [0,1] as they are probability values. The results given by the proposed encoding system for this learning problem is compared to a deterministic algorithm and another evolutionary approach. Better results are shown in terms of accuracy with respect to the former one, and accuracy and convergence speed with respect to the later one.  
International

Si 
JCR

Si 
Title

Natural Computing 
ISBN

15677818 
Impact factor JCR

0,539 
Impact info


Volume




Journal number


From page

1 
To page

16 
Month

SIN MES 
Ranking
