The main real-coded genetic algorithm (RCGA) research effort has been spent on developing efficient crossover operators. This study presents a taxonomy for this operator that groups its instances in different categories according to the way they generate the genes of the offspring from the genes of the parents. The empirical study of representative crossovers of all the categories reveals concr...

In this paper, we propose a technique for the application of the crossover operator that generates multiple descendants from two parents and selects the two best offspring to replace the parents in the new population. In crossover operator for real-coded genetic algorithms. In particular, we investigate the influence of the number of generated descendants in this operator, the xperimentation th...

A Real-coded genetic algorithm (RcGA) [1][2] is a modified genetic algorithm (GA) [3] employing realvalued vectors for representation of the chromosomes, and is widely-applied to many optimization problems [4]. The RcGA generally employs a reproduction strategy based on roulette wheel selection (RWS). However, this strategy may lose genetic diversity of population in an early stage [5], because...

Most real-coded genetic algorithm research has focused on developing effective crossover operators, and as a result, many different types of crossover operators have been proposed. Some forms of crossover operators are more suitable to tackle certain problems than others, even at the different stages of the genetic process in the same problem. For this reason, techniques that combine multiple c...

Parent-centric real-parameter crossover operators create the offspring in the neighbourhood of one of the parents, the female parent. The other parent, the male one, defines the range of the neighbourhood. With the aim of improving the behaviour of these crossover operators, we present three processes that are performed before their application. First, a female and male differentiation process ...

In Real coded genetic algorithms, some crossover operators do not work well on functions which have their optimum at the corner of the search space. To cope with this problem, we have proposed boundary extension methods which allows individuals to be located within a limited space beyond the boundary of the search space. In this paper, we give an analysis of the boundary extension methods from ...

OF THE DISSERTATION Error-Coded Algorithms for On-Line Arithmetic

Real-coded genet ic algorit hms (GAs) do not use any coding of the problem variab les, instead they work dir ectly with the variab les . The main difference in the implementation of real-coded GAs and binary-coded GAs is in their recombination op erators. Alt ho ugh a number of real-cod ed crossover implementations were suggested, most of them were developed wit h intuition and wit hout much an...

For Real-coded Genetic Algorithms, there have been proposed many crossover operators. The blend crossover (BLX-α) proposed by Eshelman and Schaffer shows good search ability for separable fitness functions. However, because of its component-wise operation, the BLX-α faces difficulties in optimization of non-separable fitness functions. The present paper proposes a novel crossover operator that ...

The crossover operator is the most innovative and relevant operator in realcoded genetic algorithms. In this work we propose a new strategy to improve the performance of this operator by the creation of virtual parents obtained from the population parameters of localisation and dispersion of the best individuals. The idea consists of mating these virtual parents with individuals of the populati...