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The tness F(r ) of the solutions is proportional to their survivability during the algorithm computation The selection operation, de ned below, will use these values to keep good solutions and discard the bad solutions For simplicity, we can use the cost of each route as the tness of each solution In order to prevent those solutions with very low tness from being discarded by selection operation of GA at the rst several computation loops of GA, the value of tness of those bad solutions is increased In this way, some bad solutions still have chances to survive at the beginning of the evolution of GA and the good parts in them will have chances to transfer to new generations For convenience, we normalize the tness of solutions to [0,1] by the following expression: F(r ) = [Cmax C(r )]Cmin (Cmax Cmin )C(r ).

(2.1.20) (2.1.21 )

_ r (_) - {Ep(r)

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In this section we first looked at how DRb s biggest advantage, its capability to easily invoke methods on a remote server, can also be a huge security concern We demonstrated how easy it would be to have malicious code run that would delete the entire contents of the server We solved that problem by setting Ruby s $SAFE level to a minimum of 1 Next we looked at how access control lists (ACLs) can help secure your services further by limiting who can and cannot gain access to that service We looked at the subtle nuances of how to build those ACLs and how they affect the entire Ruby VM running the service, not just the service you were hoping to install an ACL for We moved on to securing our DRb transactions by setting up communication between our client and server over SSL We first set up our server to accept any connection of SSL, and then later we locked it down further to allow only authorized connections over SSL So although we have managed to lock down our services quite well, there is still opportunity for malicious behavior to creep in, just like with all applications Careful planning, architecture, and testing will help, but there is no magic bullet to prevent security breaches

(2.1.22)

Es(r) =

dr'G(r, r') . k 2 (Er(r') - 1) E (r')

where Cmax and Cmin and are maximum and minimum cost of routes in each generation of population, respectively. Selection operation keeps good solutions while discard bad solutions at the same time. This operation tries to simulate natural selection in real life. Those readers with background in communications theory will easily recognize the similarities between GA and the Viterbi algorithm (VA) used to approximate maximum likelihood (ML) demodulators. Two selection operators are used in the algorithm. The rst one is based on the tness proportional model . It is also called Monte Carlo selection . The algorithm is as follows: (1) add the tness of all solutions; (2) randomly generate a number between zero and the sum of tness; this number is called the pointer of the Monte Carlo wheel; and (3) add the tness of each solution one by one until the value is greater than the pointer. Then the last solution is being selected.

(2.1.24)

E(r) = Einc(r)

dr'G(r,r') (Er(r') -1) E(r')

(2.1.25)

Using this algorithm, the higher the tness value, the bigger the chance of that solution being chosen The second selection operator used in the algorithm is the best solution reservation The best solution in the population will always survive and several duplicated copies will be generated for mutation operation In this way, the GA will always converge to a certain good solution Moreover, there will be a good chance to nd a better solution on the base of the best solution of each generation Crossover operation enables any pair of solutions in the population to have a chance to exchange part of their solution information with others Therefore, those good parts from different solutions may be combined together to create a new, better solution The two original solutions are the parents of the new solutions generated There are many crossover operators designed to solve different problems.

(2.1.26)

as the induced polarization density. When solving (2.1.25) numerically, we have to take into account the singularity of the dyadic Green's function. A volume integral equation can also be obtained by using vector and scalar potentials.

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