Sentence examples for mutation operator guided from inspiring English sources

Authors in [36] introduced a mutation operator guided by preferred regions to enhance the performance of an existing set-based evolutionary many-objective optimization algorithm.

In particular, we focus on the mutation operator guided by preferred regions, which will be expounded in "Adaptive Gaussian mutation guided by preferred regions" section.

Motivated by the above consideration, we develop an adaptive Gaussian mutation operator guided by preferred regions to enhance the exploitation capability of the algorithm.

The set-based EA that integrates preferences combined with the proposed mutation operator guided by preferred regions is called PSEA-m.

In this paper, we propose a mutation operator guided by preferred regions to improve an existing set-based evolutionary many-objective optimization algorithm that integrates preferences.

Following the above motivation, in this paper we develop a new mutation operator guided by preferred regions based on the study of Gong et al. [27].

As a result, it is essential to investigate and develop mutation operators guided by preferences.

Many researches have indicated that mutation operators play an important role in EAs [28, 29]; however, they are less well understood in many-objective evolutionary optimization, and in particular, effective mutation operators guided by preferences are rather lacking in EAs that integrate preferences.

The main contribution of this paper is that evolutionary information of a population is fully utilized to develop a mutation operator to guide its evolution towards its preferred region.

The above analysis indicates that the proposed directional mutation operator can guide the algorithm to evolve towards the preferred region, and thus steer the population towards the true Pareto front.

In the MODENA algorithm, we use 1 crossover operator (structural n-point crossover) and 2 mutation operators (point accepted mutation and error diagnosis mutation).