We present a number of bounds on convergence time for two elitist population-based Evolutionary Algorithms using a recombination operator k-Bit-Swap and a mainstream Randomized Local Search algorithm. We study the effect of distribution of elite species and population size.

Sequential decision-making problems with multiple objectives are known as multi-objective reinforcement learning. In these scenarios, decision-makers require a complete Pareto front that consists of optimal solutions. Such enables to understand the relationship between and make informed decisions from broad range However, existing methods may be unable search for solutions in concave regions or...

OBJECTIVE Language is the medium by which communication is both conveyed and received. To understand and communicate meaning it is necessary to examine the theoretical basis of word conceptualisation. The determinants of understanding language however are somewhat elusive and idiosyncratic by nature. This paper will examine briefly the development of language and how language is used in the hea...

To effectively and accurately detect and classify network intrusion data, this paper introduces a general regression neural network (GRNN) based on the artificial immune algorithm with elitist strategies (AIAE). The elitist archive and elitist crossover were combined with the artificial immune algorithm (AIA) to produce the AIAE-GRNN algorithm, with the aim of improving its adaptivity and accur...

In this paper modified version of roulette selection for evolution algorithms the fan selection, is presented. This method depends on increase of survive probability of better individuals at the expense of worse individuals. Test functions chosen from literature are used for determination of quality of proposed method. Results obtained for fan selection are compared with results obtained using ...

We present a number of bounds on convergence time for two elitist population-based Evolutionary Algorithms using a recombination operator k-Bit-Swap and a mainstream Randomized Local Search algorithm. We study the effect of distribution of elite species and population size.

The paper is devoted to upper bounds on expected first hitting times of the sets of local or global optima for non-elitist genetic algorithms with very high selection pressure. The obtained results extend the range of situations where the upper bounds on the expected runtime are known for genetic algorithms and apply, in particular, to Canonical Genetic Algorithm.

Almost all approaches to multiobjective optimization are based on Genetic Algorithms, and implementations based on Evolution Strategies (ESs) are very rare. In this paper, a new approach to multiobjective optimization, based on ESs, is presented. The comparisons with other algorithms indicate a good performance of the Multiobjective Elitist