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that the GA was more effective in the search with fewer string evaluations than the single hill climbers The reason for this result could be that each of the hill climbers searches a different space than the other hill climbers, and there is no crossover technique to distribute the good searches among the hill climbers GAs are parallel search techniques that start with a set of random potential solutions and use special search operators (evaluation, selection, crossover, and mutation) to bias the search toward the promising solutions At any given time, unlike any optimization approach, a GA has several promising potential solutions (equal to population size) as opposed to one optimal solution Each population member in a GA is a potential solution A GA tarts with a random set of the population An evaluation operator is then applied to evaluate the tness of each individual In the case of learning connection weights for ANN for classi cation, the evaluation function is the number of correctly classi ed cases A selection operator is then applied to select the population members with higher tness (so that they can be assigned higher probability for survival) Under the selection operator, individual population members may be born and be allowed to live or to die Several selection operators are reported in the literature; the operators are proportionate reproduction, ranking selection, tournament selection, and steadystate selection Among the popular selection operators are ranking and tournament selection Both ranking and tournament selection maintain strong population tness growth potential under normal conditions The tournament selection operator, however, requires lower computational overhead In tournament selection, a random pair of individuals is selected and the member with the better tness of the two is admitted to the pool of individuals for further genetic processing The process is repeated in a way that the population size remains constant and the best individual in the population always survives For our case, we used the tournament selection operator After the selection operator is applied, the new population special operators, called crossover and mutation, are applied with a certain probability For applying the crossover operator, the status of each population member is determined Each population member is assigned a status as a survivor or a nonsurvivor The number of population members equal to survivor status is approximately equal to population size (1 probability of crossover) The number of nonsurviving members is approximately equal to population size probability of crossover The nonsurviving members in a population are then replaced by applying crossover operators to randomly selected surviving members Several crossover operators exist We describe and use three different crossover operators in our case The crossover operators used in our research are the one-point crossover in which two surviving parents and a crossover point are randomly selected For each parent, the genes on the right-hand side of the crossover point are exchanged to produce two children The second operator is the uniform crossover In the uniform crossover, two surviving parents are randomly selected and exchanging the genes in the two parents produces two children; probability of exchanging any given gene in a parent is 05.

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and how and when to apply pressure to the system To productively engage in the political process, nonprofits need to understand the context, the process, and the people involved in each particular decision It is also important for a nonprofit to understand its role in the policy market, specifically where, how, and when the nonprofit can play a role, or influence the process US Policymaking Process One step in developing political competence is having a general understanding of the US policymaking process In this context, policy is defined as laws and enacted legislation, the rules and regulations developed by the agencies to implement legislation or operate the government or its programs, and the legal decisions made at the judicial level regarding laws, rules, or regulations The policymaking process can be, and has been, conceptualized in a number of models that describe the components of the process and the relationships among the components One conceptual model includes five stages: problem definition, agenda setting, policy adoption, policy implementation, and policy evaluation (Eyestone, 1978) Although the use of the word stage may imply that the process is an orderly one, moving in a linear fashion, that is not necessarily the case in many instances The process is a circular process, not a linear one, with later stages occurring before earlier ones For example, policy evaluation is the fifth stage in the model, and problem definition is the first In some cases, evaluation of an existing policy may stimulate redefinition of the problem the policy was supposed to address ( JenkinsSmith, 1993) Nonprofits can be involved or influence activity anywhere along the policymaking spectrum, from problem definition to modifying policy Problem Definition In the problem definition stage in the policymaking process, the goal is to define the problem to describe its salient features and characteristics However, individuals or groups may look at a problem or issue and define it in substantially different ways Some may not even view the issue as a problem requiring solving How one defines the problem has implications for how one solves a problem, and controlling problem definition is one of the ways to control the problem solution Agenda Setting In he agenda setting stage in the policymaking process, a particular problem or issue emerges out of the almost unlimited number of existing issues as one of the problems that policymakers will focus on, or will try to solve If an issue or problem never comes to the attention of the policymakers, however, it will not be able to proceed through the next stages in the process Having the problem placed on the policymaking agenda is thus a critical stage in the policymaking process.

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