In this paper the effects of elitism in the Nondominated Sorting Genetic Algorithm (NSGA) are analyzed. Three different kinds of elitism: standard, clustering and Parks & Miller techniques are investigated using two test problems. For the studied problems, the Parks & Miller mechanism generated the best results. Finally, the NSGA with Parks & Miller elitism was applied to determine the nondomin...

This paper shows that the design of education policy involves a potential conflict between welfare and social mobility. We consider a setting in which social mobility is maximized under the least elitist public education system, whereas welfare maximization calls for the most elitist system. We show that when private education is available, the degree of elitism that maximizes social mobility i...

Elitism, a technique which consists of copying, unchanged, one or more of the most fit individuals from one generation to the next, is widely used in generational evolutionary algorithms, including Genetic Programming (GP). Elitism ensures that the best individuals discovered in a generation (and hence in the whole run) are not lost, and, perhaps even more importantly, are made available to new...

Scholars who study populism from an ‘ideational approach’ consider as a set of ideas based on moralised anti-establishment thinking and strong people-centrist view politics. From this perspective, at theoretical level, populist attitudes have the following two main contrasts: pluralism elitism. In article, we investigate ideological consistency populism-pluralism-elitism among voters. Analysing...

#1 Dean (R&D), Chairman & Professor (CSE/IT/MCA), H.C.T.M., Kaithal (Haryana), India #2 Professor, Deptt. of Computer Science & Applications, K.U., Kurukshetra (Haryana), India #3 Assistant Professor, Deptt. of Computer Science & Applications, K.U., Kurukshetra (Haryana), India __________________________________________________________________________________________________ AbstractThe use of ...

NSGA methodology discussed in Section 3.1 suffers from three weaknesses: computational complexity, non-elitist approach and the need to specify a sharing parameter. An improved version of NSGA known as NSGA-II, which resolved the above problems and uses elitism to create a diverse Pareto-optimal front, has been subsequently presented (Deb et al 2002). The main features of NSGA-II are low comput...