Crossover and mutation are two of the most important genetic operators found in genetic algorithms. There has been much debate as to which of these is practically and theoretically more effective. This literature review highlights the principal milestones of this debate. The conclusion we reach is that there is no evidence to show that either operator is better than the other, and that both ope...

In this paper we present two sets of empirical data evaluating the performance of a new Cleanup operator for evolutionary approaches to the travelling salesman problem (TSP). For raw data we have used standard road mileage charts of the USA, Great Britain and Ireland, which enable us to generate a reference table with appropriate city to city distances. A wide variety of standard genetic parame...

The traditional concept of a genetic algorithm (GA) is that of selection, crossover and mutation. However, a limited amount of data from the literature has suggested that the niche for the beneficial effect of crossover upon GA performance may be smaller than has traditionally been held. Based upon previous results on not-linear-separable problems we decided to explore this by comparing two tes...

In this paper, we develop a novel clonal algorithm for multiobjective optimization (NCMO) which is improved from three approaches, i.e., dynamic mutation probability, dynamic simulated binary crossover (D-SBX) operator and hybrid mutation operator combining with Gaussian and polynomial mutations (GP-HM operator). Among them, the GP-HM operator is controlled by the dynamic mutation probability. ...

In this paper, we have proposed a new genetic algorithm for p-median location problem. In this regard, prior genetic algorithms were designed for p-median location problem by proposing several methods that are used in generation of initial population, crossover and mutation operators, and new operator socalled re-allocation has been incorporated into the algorithm that causes to find the optima...

This paper presents a comparison in the performance analysis between a newly developed mutation operator called Scaled Truncated Pareto Mutation (STPM) and an existing mutation operator called Log Logistic Mutation (LLM). STPM is used with Laplace Crossover (LX) taken from literature to form a new generational RCGA called LX-STPM. The performance of LX-STPM is compared with an existing RCGA cal...

Abstract The problem of finding roots equations has always been an important research in the fields scientific and engineering calculations. For standard differential evolution algorithm cannot balance convergence speed accuracy solution, improved is proposed. First, one-half rule introduced mutation process, that is, half individuals perform evolutionary mutation, other strategy reorganization...

In genetic algorithms, commonly used crossover operators such as one-point, two-point and uniform crossover operator are likely to destroy the information obtained in the evolution because of their random choices of crossover points. To overcome this defect, a new adaptive crossover operator based on the Rough Set theory is proposed in this paper. By using this specialized crossover operator, u...

Genetic Algorithms are biologically inspired optimization algorithms. Performance of genetic algorithms mainly depends on type of genetic operators – Selection, Crossover, Mutation and Replacement used in it. Crossover operators are used to bring diversity in the population. This paper studies different crossover operators and then proposes a hybrid crossover operator that incorporates knowledg...