Abstract While archaeology is certainly a politically conscious discipline, with various members involved in political and activist movements, especially focusing on gender race issues, little has been said, discussed, or done regards to class elitism. In fact, it seems that since the advent of postmodernity 1970s, elitism have become moot topics. The aim this article reflect upon class-based d...

A Multiclass classifier is an approach for designing classifiers for a m-class (m>=2) problem using genetic programming (GP). In this paper we proposed three methods named Triple Tournament Method, Special Mutation Method and a Step Wise Crossover method. In Special Mutation technique we are generating the two child from single parent and selecting the one child on the basis of fitness and also...

In recent years, there has been a growing interest in addressing dynamic optimization problems (DOPs) using evolutionary algorithms (EAs). Several approaches have been developed for EAs to increase the diversity of the population and enhance the performance of the algorithm for DOPs. Among these approaches, immigrants schemes have been found beneficial for EAs for DOPs. In this paper, random, e...

Elitism in education is often applied by a different and prefered approach to students belonging the so-called elite. This elite segregated developed, at expense of needs other students. Can elitism be one problems pedagogical practice with intellectually gifted ordinary inclusive primary schools? We carried out qualitative research environment schools observing school lessons interviews teache...

This paper reports the development and implementation of a Genetic Algorithm (GA) based on welding sequence optimization in which a structural deformation is computed as a fitness function. Moreover, a thermo-mechanical finite element analysis (FEA) was used to predict deformation. Elitism selection approach has been used to ensure that the three best individuals are copied over once into the n...

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 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.