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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.
A simplified version of an Evolution Strategy, using fixed point arithmetic and a hardware-friendly mutation operator, has been chosen as the search algorithm.
However, the mutation operator has trade-offs in slowing down the learning process.
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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.
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.
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.
Over past few years several mutation operators have been proposed to improve the performance of EP on a wide variety of numerical benchmarks.
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