Classifier systems are rule-based control systems for the learning of more or less complex tasks. They evolve in an autonomous way through solution without any external help. The knowledge base (the population) consists of rule sets (the individuals) randomly generated. The population evolves due to the use of a genetic algorithm. Solving complex problems with classifier systems involves proble...

Addressing dynamic optimization problems has been a challenging task for the genetic algorithm community. Over the years, several approaches have been developed into genetic algorithms to enhance their performance in dynamic environments. One major approach is to maintain the diversity of the population, e.g., via random immigrants. This paper proposes an elitism-based immigrants scheme for gen...

This paper proposes an extension of the Gravitational Search Algorithm (GSA) to multiobjective optimization problems. The new algorithm, called Non-dominated Sorting GSA (NSGSA), utilizes the non-dominated sorting concept to update the gravitational acceleration of the particles. An external archive is also used to store the Pareto optimal solutions and to provide some elitism. It also guides t...

This paper analyzes the convergence of metaheuristics used for multiobjective optimization problems in which the transition probabilities use a uniform mutation rule. We prove that these algorithms converge only if elitism is used.

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...

This paper presents a new genetic approach based on arithmetic crossover for solving the economic dispatch and environmentally constrained economic dispatch problems. Elitism, arithmetic crossover and mutation are used in the genetic algorithm to generate successive sets of possible operating policies. The proposed technique improves the quality of the solution. The employed arithmetic crossove...

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...

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 ...

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...