Most discrete evolutionary algorithms (EAs) implement elitism, meaning that they make the biologically implausible assumption fittest individuals never die. While elitism favours exploitation and ensures best seen solutions are not lost, it has been widely conjectured non-elitism is necessary to explore promising fitness valleys without getting stuck in local optima. Determining when non-elitis...

In this paper, we describe a multi-objective evolutionary algorithm, that uses clustering selection and does not need any additional parameter like others. It clusters the population into a exible number of clusters employing x-means from [Pelleg and Moore, 2000]. First, the selective tness is assigned to clusters and in second place to individuals of clusters. We show three hybrid variants inc...

Water distribution systems (WDSs) are costly infrastructure, and much attention has been given to the application of optimisation methods to minimise design costs. In previous studies, Ant Colony Optimisation (ACO) has been found to perform extremely competitively for WDS optimisation. In this paper, five ACO algorithms are tested: one basic algorithm (Ant System) and four more advanced algorit...

The paper presents an exponential pheromone deposition rule to modify the basic ant system algorithm which employs constant deposition rule. A stability analysis using differential equation is carried out to find out the values of parameters that make the ant system dynamics stable for both kinds of deposition rule. A roadmap of connected cities is chosen as the problem environment where the sh...

We present a comparative study of seven evolutionary algorithms (Generational Genetic, Elitist Genetic, Steady State Genetic, Evolution Strategy, Evolution Strategy, generational and elitist Covariance Matrix Adaptation) for automatic calibration of a constrained cellular automaton (CCA), whose performance are assessed in terms of two fitness metrics (based on Kappa statistics and Lee-Salee Ind...

The problem of density estimation on R is considered. Adopting the maxiset point of view, we focus on performance of adaptive procedures. Any rule which consists in neglecting the wavelet empirical coefficients smaller than a sequence of thresholds vn will be called an elitist rule. We prove that for such a procedure the maximal space for the rate v n , with 0 < α < 1, is always contained in th...

It is shown by means of Markov c hain analysis that unimodal binary long path problems can be solved by m utation and elitist selection in a polynomially bounded number of trials on average.

Objective-space discretization is a popular method to control the elitist archive size for evolutionary multi-objective optimization and avoid problems with convergence. By setting the level of discretization, the proximity and diversity of the Pareto approximation set can be controlled. This paper proposes an adaptive archiving strategy which is developed from a rigid-grid discretization mecha...