Nevertheless, search history has been sparsely exploited and studied in existing methods. To enable a better solution for the population without increasing the number of FEs, the way of exploiting the search history truly matters. Also, the computational cost of a single FE can be high when the fitness functions are complicated. That is, no additional FEs are allowed for improving the search performance. For instance, exploiting search history can be useful for improving the search procedure under a limited budget of fitness evaluations (FEs).

Such accumulated search data constitute search history which can be very informative and valuable for boosting the overall performance. Because of the offspring generation, a large number of candidate solutions (i.e., individuals) are sampled, accompanied by corresponding fitness values, genetic information, and genealogy information. The population quality is gradually improved throughout the exploration process, which can be viewed as a stochastic population-based generation-and-test process.

The exploration process of EAs imitates the natural selection process, which is realized by conducting the offspring generation and survivor individual selection alternately and iteratively. IntroductionĮvolutionary algorithms (EAs) have been shown to be generic and effective to search for global optima in the complex search space theoretically and practically. Also, the induced additional runtime is negligible compared to the total processing time. Quantitative results show that our SHX can significantly enhance the performance of RCGA, in terms of both accuracy and convergence speed.

We experimentally verify the effectiveness of SHX over 15 benchmark functions. Since no additional fitness evaluations are needed, SHX is favorable for the tasks with limited budget or expensive fitness evaluations. In essence, the proposed SHX is a data-driven method which exploits the search history to perform offspring selection after the offspring generation. In particular, the search history is clustered, and each cluster is assigned a score for SHX. We introduce a simple yet effective crossover model driven by the search history (abbreviated as SHX). Specifically, survivor individuals over the past few generations are collected and stored in the archive to form the search history. To boost the performance of offspring generation in the real-coded genetic algorithm (RCGA), in this paper, we propose to exploit the search history cached so far in an online style during the iteration. In evolutionary algorithms, genetic operators iteratively generate new offspring which constitute a potentially valuable set of search history.

The offspring 2020