Analysing mutation schemes for real-parameter genetic algorithms

Abstract

Mutation is an important operator in genetic algorithms (GAs), as it ensures maintenance of diversity in evolving populations of GAs. Real-parameter GAs (RGAs) handle real-valued variables directly without going to in a binary string representation of variables. Although RGAs were first suggested in early nineties, the mutation operator is still implemented variablewise and independently for each variable. In this paper, we investigate the effect of five different mutation schemes for RGAs for two different mutation operators. Based on extensive simulation studies, it is observed that a mutation clock implementation is computationally quick and also efficient in finding a solution close to the optimum on four different problems used in this study for both mutation operators. Moreover, parametric studies with their associated parameters reveal suitable working ranges of the parameters. Interestingly, both mutation operators with their respective optimal parameter settings are found to possess a similar inherent probability of offspring creation, a matter that is believed to the reason for their superior working. This study signifies that the longsuggested mutation clock operator should be considered as a valuable mutation operator for RGAs.

Automatic Component-Wise Design of Multiobjective Evolutionary Algorithms

“…Furthermore, different mutation schemes can be used by MOEAs for real-parameter optimization [29]. Here, we implement two options: (i) bitwise, which sets the mutation probability per variable such that on average one variable is mutated per individual chosen for mutation; and (ii) fixed, where the mutation probability per individual mutated is set by the user as a parameter p v ∈ [0.01, 1].…”

Automatic Generation of Tests to Exploit XML Injection Vulnerabilities in Web Applications

“…Popular genetic operators for RGA are the binary tournament selection, the single arithmetic crossover [26], and Gaussian mutation [27]. Therefore, the selection mechanism is the same as in SGA, whereas crossover and mutation operators are different.…”

“…With the uniform mutation, a character is replaced with any other character in the alphabet. Instead, the gaussian mutation is defined for numerical values, which are replaced with other numerical values but according to a Gaussian distribution [27]. In our case, let S i = c 1 , c 2 , .…”

“…where δ(µ, σ) is a normally distributed random number with mean µ = 0 and variance σ [27]. In other words, the new ASCII code is generated by adding a normally distributed delta to the original ASCII code c j .…”

Strategies to Automatically Derive A Process Model From A Configurable Process Model Based on Event Data

“…Genetic algorithms (GA) are search algorithms that imitate the process of natural selection in nature, belonging to the class of evolutionary algorithms [25][26][27]. In this subsection, we present a GA approach to find a suitable configuration for a CPT model given a specific event log.…”