A Branch-and-Cut Algorithm for the Median-Path Problem by Avella P., Boccia M., Sforza A. PDF

By Avella P., Boccia M., Sforza A.

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Deceptive Problems Two types of deceptive problem are also considered. The first deceptive problem is a minimal deceptive problem (mDP) that is formed by concatenating twenty copies of the minimal deceptive function [38] shown in Fig. 4(a). The second deceptive problem is a fully deceptive problem composed of twenty copies of the modified 3-bit trap function depicted in Fig. 4(b). The purpose of the modification is to fulfill the assumptions (described in Sect. 3) for the target problem. In the deceptive problems, one-point crossover is used for avoiding the excessive disruption of BBs [45].

Since the order of the BBs is one, any crossover does not disrupt them. 5 is employed for achieving the maximum (BB-wise) mixing rate. 3 depicts the results of the population-sizing model on a 100-bit one-max problem. It is seen that the population the experimental results are in agreement with the theory, especially as the population size N increases. Moreover, the practical population-sizing model is perfectly matched with Harik’s model because their probabilities of correct decision are equivalent (as explained in Sect.

4 Elitist Compact Genetic Algorithms This chapter describes two elitism-based compact genetic algorithms (cGAs) – persistent elitist compact genetic algorithm (pe-cGA), and nonpersistent elitist compact genetic algorithm (ne-cGA). The aim is to design efficient compacttype GAs by treating them as simple estimation of distribution algorithms (EDAs) for solving difficult optimization problems without compromising on memory and computation costs. Difficult problems have the following characteristics: 1) full deception, 2) interdependence (of decision variables), 3) multimodality, and 4) symmetry.

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A Branch-and-Cut Algorithm for the Median-Path Problem by Avella P., Boccia M., Sforza A.


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