Ordinarily, Genetic Programming uses little or no mutation. Crossover is the predominant operator. This study tests the eeect of a very aggressive use of the mutation operator on the generalization performance of our Compiling Genetic Programming System ('CPGS'). We ran our tests on two benchmark classiication problems on very sparse training sets. In all, we performed 240 complete runs of popu...

Diversity in evolutionary algorithms is a critical issue related to the performance obtained during the search process and strongly linked to convergence issues. The lack of the required diversity has been traditionally linked to problematic situations such as early stopping in the presence of local optima (usually faced when the number of individuals in the population is insufficient to deal w...

Evolutionary algorithms solve problems by simulating the evolution of a population candidate solutions. We focus on evolving permutations for ordering such as traveling salesperson problem (TSP), well assignment quadratic (QAP) and largest common subgraph (LCS). propose cycle mutation, new mutation operator whose inspiration is well-known crossover operator, concept permutation cycle. use fitne...

Software testing is a significant phase in any software development lifecycle irrespective of the type of software being developed. The main goal of software testing phase is to minimize the software faults in a system and increase its reliability. A software fault is an unintended mistake that causes failure of the system or any system component. Therefore, it is vital that the system is teste...

Evolutionary programming (EP) has been successfully applied to many parameter optimization problems. We propose a mean mutation operator, consisting of a linear combination of Gaussian and Cauchy mutations. Preliminary results indicate that both the adaptive and non-adaptive versions of the mean mutation operator are capable of producing solutions that are as good as, or better than those produ...

Genetic algorithms are optimisation algorithms and mimic the natural process of evolution. Important operators used in genetic algorithms are selection, crossover and mutation. Selection operator is used to select the individuals from a population to create a mating pool which will participate in reproduction process. Crossover and mutation operators are used to introduce diversity in the popul...

This paper introduces a new method of performing mutation in a real-coded Genetic Algorithm (GA), a method driven by Principal Component Analysis (PCA). We present both theoretical and empirical results which show that our mutation operator attains higher levels of diversity in the search space, as compared to other mutation operators, meaning that by employing our mutation operator we maintain...

In this article, a novel hybrid genetic algorithm is proposed. The selection operator, crossover operator andmutation operator of the genetic algorithm have effectively been improved according to features of Sudoku puzzles. The improved selection operator has impaired the similarity of the selected chromosome and optimal chromosome in the current population such that the chromosome with more ab...

The dynamic behavior of mutation and crossover is investigated with the Breeder Genetic Algorithm. The main emphasis is on binary functions. The genetic operators are compared near their optimal performance. It is shown that mutation is most eecient in small populations. Crossover critically depends on the size of the population. Mutation is the more robust search operator. But the BGA combines...

The dynamic behavior of mutation and crossover is investigated with the Breeder Genetic Algorithm The main emphasis is on binary functions The genetic operators are compared near their optimal performance It is shown that mutation is most e cient in small populations Crossover critically depends on the size of the population Mutation is the more robust search operator But the BGA combines the t...