We present a covariant form for the dynamics of a canonical GA of arbitrary cardinality, showing how each genetic operator can be uniquely represented by a mathematical object - a tensor - that transforms simply under a general linear coordinate transformation. For mutation and recombination these tensors can be written as tensor products of the analogous tensors for one-bit strings thus giving...

Several studies on the variations of the crossover operator have shown that each of them presents speci£c properties that are interesting under particular circumstances. The advantages of each operator over others are often contradictory and the best operator depends on the problem being solved. This paper is based on the assumption that a combination of several crossover operators can take adv...

Like other learning paradigms, the performance of the genetic algorithms (GAs) is dependent on the parameter choice, on the problem representation, and on the fitness landscape. Accordingly, a GA can show good or weak results even when applied on the same problem. Following this idea, the crossover operator plays an important role, and its study is the object of the present paper. A mathematica...

The present study is an attempt to propose a mutation-based real-coded genetic algorithm (MBRCGA) for sizing and layout optimization of planar and spatial truss structures. The Gaussian mutation operator is used to create the reproduction operators. An adaptive tournament selection mechanism in combination with adaptive Gaussian mutation operators are proposed to achieve an effective search in ...

Gene expression programming (GEP) uses mutation, transposition , and crossover to create variation. Although there exists a large body of work in genetic algorithms concerning the roles of mutation and recombination, these results not only do not apply to GEP due to the genotype/ phenotype representation but also seem to contradict the GEP experience. Therefore, and given the diversity of GEP o...

In recent years, a variety of semantic operators have been successfully developed to improve the performance of GP. This work presents a new semantic operator based on the semantic crossover based on the partial derivative error. The operator presented here uses the information of the second partial derivative to choose a crossover point in the second parent. The results show an improvement wit...

Self-adaptation is an essential feature of natural evolution. However, in the context of function optimization, self-adaptation features of evolutionary search algorithms have been explored mainly with evolution strategy (ES) and evolutionary programming (EP). In this paper, we demonstrate the self-adaptive feature of real-parameter genetic algorithms (GAs) using a simulated binary crossover (S...

In recent theoretical and experimental work on schemata in genetic programming we have proposed a new simpler form of crossover in which the same crossover point is selected in both parent programs. We call this operator one-point crossover because of its similarity with the corresponding operator in genetic algorithms. One point crossover presents very interesting properties from the theory po...

Traditionally, crossover operators are based on combination--an operator takes parts from two parents and combines them into an offspring. This paper presents a series of crossover operators based on commonality--an operator preserves the common parts from two parents and uses them as a base on which an offspring solution is built. Experiments on benchmark sequencing problems show that these ne...

Storage resource scheduling is lack of flexibility, and user’ demand for different data is vary. In cloud computing storage resource scheduling process, these data files to be equally are irrational, so it needs a dynamic storage resource scheduling mechanism to distinguish requirements of different data. For the file hot issues that a surge request results in the copy number of the current fil...