| This paper ooers suucient conditions to prove global convergence of non{elitist evolutionary algorithms. If these conditions can be applied they yield bounds of the convergence rate as a by{product. This is demonstrated by an example that can be calculated exactly. KeyWords| global convergence, non{elitist evolutionary algorithm , martingale theory

This paper introduces a new technique called adaptive elitist-population search method. This technique allows unimodal function optimization methods to be extended to efficiently explore multiple optima of multimodal problems. It is based on the concept of adaptively adjusting the population size according to the individuals’ dissimilarity and a novel direction dependent elitist genetic operato...

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...

Epistasis and NK-Landscapes in the context of multiobjective evolutionary algorithms are almost unexplored subjects. Here we present an extension of Kauffman’s NK-Landscapes to multiobjective MNK-Landscapes in order to use them as a benchmark tool and as a mean to understand better the working principles of multiobjective evolutionary algorithms (MOEAs). In this work we present an elitist multi...

Black-box complexity theory provides lower bounds for the runtime of black-box optimizers like evolutionary algorithms and other search heuristics and serves as an inspiration for the design of new genetic algorithms. Several black-box models covering different classes of algorithms exist, each highlighting a different aspect of the algorithms under considerations. In this work we add to the ex...

A novel framework for automatic articulatory-acoustic feature extraction has been developed for enhancing the accuracy of placeand manner-of-articulation classification in spoken language. The ‘‘elitist’’ approach provides a principled means of selecting frames for which multi-layer perceptron, neural-network classifiers are highly confident. Using this method it is possible to achieve a frame-...

The covariance matrix adaptation evolution strategy (CMA-ES) is one of the most powerful evolutionary algorithms for real-valued single-objective optimization. In this paper, we develop a variant of the CMA-ES for multi-objective optimization (MOO). We first introduce a single-objective, elitist CMA-ES using plus-selection and step size control based on a success rule. This algorithm is compare...

Ant Colony Optimization (ACO) has been applied successfully in solving the Traveling Salesman Problem. Marco Dorigo et al. used Ant System (AS) to explore the Symmetric Traveling Salesman Problem and found that the use of a small number of elitist ants can improve algorithm performance. The elitist ants take advantage of global knowledge of the best tour found to date and reinforce this tour wi...

In this paper, a novel approach based on handling constraints as objectives together with a modified Parks & Miller elitist technique, to solve constrained multiobjective optimization problems, is analyzed with Niched Pareto Genetic Algorithm. The performance of this approach is compared with the classical procedure of handling constraints that is the exterior penalty function method. Results a...