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Recently, genetic algorithms using standard crossover and mutation operators have been proposed to tackle this problem.
Specially designed crossover and mutation operators have been created to work with the non-standard genome structure.
In this context, in this section, we first summarise how mutation operators have been designed for procedural and OO paradigms.
Over past few years several mutation operators have been proposed to improve the performance of EP on a wide variety of numerical benchmarks.
Different mutation operators have been proposed in evolutionary programming, but for each operator there are some types of optimization problems that cannot be solved efficiently.
Gaussian and Cauchy mutation operators have been included in different subpopulations to improve the search diversity and avoid the local optimum.
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Since the extended multi-objective model belongs to NP-hard problems, an archived multi-objective simulated annealing (AMOSA) with an effective solution structure and seven mutation operators has been developed to solve the extended model and produce non-dominated solutions.
Because copying using mechanism C has high fidelity, explicit mutation operators had to be introduced.
Each μJava mutation operator has an acronym for identification.
Updating matrices of a solution after implementing a mutation operator has been another difference.
Finally, the mutation operator has the role of keeping a minimum diversity level of individuals in a population.
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