As one of the most popular and well-recognized clustering methods, fuzzy C-means (FCM) clustering algorithm is the basis of other fuzzy clustering analysis methods in theory and application respects. However, FCM algorithm is essentially a local search optimization algorithm. Therefore, sometimes, it may fail to find the global optimum. For the purpose of getting over the disadvantages of FCM a...

Previous works have shown the eeciency of a new approach for the Genetic Algorithms, the Dual Genetic Algorithms, in the multiobjective optimization context. Dual Genetic Algorithms make use of a meta level to enhance the expressiveness of schemata, entities implicitly handle by Genetic Algorithms. In this paper, we show that this approach, coupled with a new method, Pareto Elitism, leads to ve...

This work introduces a new evolutionary approach to solving the problems of multiobjective optimization. Novelty of the proposed method consists in the application of an evolutionary multi-agent system equipped with the mechanism(s) of elitism, instead of its ’classical’ (non-elitist) version. In the paper the model of an elitist evolutionary multi-agent system is described together with its sa...

Abstract We investigate the effects of populist messages that (a) stress centrality “ordinary” people, (b) shift blame to “corrupt” elites, or (c) combine people and antielitist cues on 3 dimensions attitudes: anti-elitism, homogeneous popular sovereignty. conducted an extensive 15-country experiment in which we manipulated communication as social identity frames (N = 7,271). Multilevel analyse...

Socially, the word"elite" is broadly used to refer to a superior group of peopleregarding skills or privileges and associates with other terms such aspolitical systems, "authorities", "minority favorite".Elites are cautious and opportunistic people. They are in every way and situationregarded as people's favorites. Focusing on concept of “elite” and “elitism”, theauthor in this paper, attempts ...

During last decades, developing multi-objective evolutionary algorithms for optimization problems has found considerable attention. Flexible job shop scheduling problem, as an important scheduling optimization problem, has found this attention too. However, most of the multi-objective algorithms that are developed for this problem use nonprofessional approaches. In another words, most of them c...

In search based test data generation, the problem of test data generation is reduced to that of function minimization or maximization.Traditionally, for branch testing, the problem of test data generation has been formulated as a minimization problem. In this paper we define an alternate maximization formulation and experimentally compare it with the minimization formulation. We use genetic alg...

This paper proposes a new type of mutation operator, FEDS (Fitness, Elitism, Depth, and Size) mutation in genetic programming. The concept behind the new mutation operator is inspired from already introduced FEDS crossover operator to handle the problem of code bloating. FEDS mutation operates by using local elitism replacement in combination with depth limit and size of the trees to reduce blo...

This paper presents an application of elitist Non-dominated Sorting Genetic Algorithm (NSGA-II), to efficiently schedule a set of independent tasks in a heterogeneous distributed computing system. This scheduling problem is a bi-objective problem considering two objectives. The first objective is minimization of makespan and the second one being the minimization of flowtime. As a multi-objectiv...

Surprisingly, general search heuristics often solve combinatorial problems quite sufficiently, although they do not outperform specialized algorithms. Here, the behavior of simple randomized optimizers on the maximum clique problem on planar graphs is investigated rigorously. The focus is on the worst-, average-, and semi-average-case behaviors. In semi-random planar graph models an adversary i...