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Secure routing protocol (SRP) [54] is a protocol that can be used as an extension of a variety of routing protocols in order to add various security related attributes to these protocols In our discussion we focus on the use of SRP for making DSR more secure

Beyer [61, 62, 63, 64] provided the rst theoretical analyses of ES In [61] Beyer made one of the rst statements claiming that ES are robust against disturbances of the search landscape B ck and Hammel [42] provided some of the rst empirical results a to show that low levels of noise in the objective function do not have an in uence on performance Under higher noise levels, they recommended that the ( , )-ES, with > 1 be used, due to its ability to maintain diversity In a more elaborate study, B ck [40] recon rmed these statements and showed that the ( , )-ES was perfectly a capable to track dynamic optima for a number of di erent dynamic environments Markon et al [562] showed that ( + )-ES with threshold-selection is capable of optimizing noisy functions With threshold-selection an o spring is selected only if its performance is better than its parents by a given margin Arnold and Beyer [32] provided a study of the performance of ES on noisy functions, where noise is generated from a Gaussian, Cauchy, or 2 distribution Beyer [65] and 1 Arnold [31] showed that rescaled mutations improve the performance of ES on noisy functions Under rescaled mutations, mutational step sizes are multiplied by a scaling factor (which is greater than one) to allow larger step sizes, while preventing that these large step sizes are inherited by o spring Instead, the smaller step sizes are inherited

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In order to locate multiple solutions, Aichholzer et al [12] developed a multipopulation ES, referred to as the ( / , , )-ES, where is the number of subpopulations and speci es the number of generations that each individual is allowed to survive Given

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clusters, recombination proceeds as follows: One of the clusters is randomly selected, and roulette wheel selection is used to select one of the individuals from this cluster as one of the parents The second parent is selected from any cluster, but such that individuals from remote clusters are selected with a low probability Over time, clusters tend to form over local minima Shir and B ck [785] proposed a dynamic niching algorithm for ES that identi es tnessa peaks using a peak identi cation algorithm Each tness-peak will correspond to one of the niches For each generation, the dynamic niche ES applies the following steps: The mutation operator is applied to each individual After determination of the tness of each individual, the dynamic peak identi cation algorithm is applied to identify nK niches Individuals are assigned to their closest niche, after which recombination is applied within niche boundaries If required, niches are populated with randomly generated individuals If nK niches have to be formed, they are created such that their peaks are a distance r of at least n nK from one another, where x r= 1 2

(xmax,j xmin,j )2

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