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...

Particle swarm optimization (PSO) has been widely used mainly due to its simple concept and its ability to converge to reasonable solution fast. However, this algorithm is always inefficient while optimizing complex global optimization problems because it is easy to be trapped into local optima. Researches into the combination of evolutionary operators with PSO is one of the most significant an...

As described in [Col 96], classical single point crossover (SPC) can be implemented through a sequence of mathematical operations; generalization of such a mathematical description originates the generalized crossover (GC). In this paper, after a brief recall to GC, we perform either a theoretical or experimental investigation of its properties. The first theoretical result we present is an exp...

Partial separation is a mathematical technique that has been used in optimization for the last 15 years. On the other hand, genetic algorithms are widely used as global optimizers. This paper investigates how partial separability can be used in conjunction with GA. In the first part of this paper, a crossover operator designed to solve partially separable global optimization problems involving ...

In this paper we propose a new crossover operator for real coded evolutionary algorithms that is based on a parabolic probability density function. This density function depends on two real parameters α and β which have the capacity to achieve exploration and exploitation dynamically during the evolutionary process in relation to the best individuals. In other words, the proposed crossover oper...

In recent days, Swarm Intelligence plays an important role in solving many real life optimization problems. Particle Swarm Optimization (PSO) is swarm intelligence based search and optimization algorithm which is used to solve global optimization problems. But due to lack of population diversity and premature convergence it is often trapped into local optima. We can increase diversity and preve...

As a fundamental operator in genetic algorithms (GAs), crossover may not only make an existing schema survive, but also construct a new one from other existing schemata. Unfortunately, the existing schema theorems do not exactly quantify the positive effects of schema construction by a crossover. Consequently, they cannot well characterize the evolution of a schema. In this paper, a new ternary...

Abst ract . T he success of binary-coded gene t ic algorithms (GA s) in problems having discrete sear ch space largely depends on the coding used to represent the prob lem var iables and on the crossover ope ra tor that propagates buildin g blocks from parent strings to children st rings . In solving optimization problems having continuous search space, binary-coded GAs discr et ize the search ...

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.

This paper presents a study of the effectiveness of a recently presented crossover operator for the GAuGE system. This crossover, unlike the traditional crossover employed previously, preserves the association of positions and values which exists in GAuGE genotype strings, and as such is more adequate for problems where the meaning of an allele is dependent on its placement in the phenotype str...