Abstract
Genetic algorithm (GA) has become very popular for tackling computationally expensive numerical optimization problems. To optimize their performance and convergence rate, we propose and demonstrate an embedded stage-specific gene expression framework (SGEF) with a two-tier coding scheme and a filter operator based on variational auto-encoder. For the first time, the structure of variational auto-encoder is used to map the parent population of a genetic algorithm from the uniformly distributed high dimensions to the Gaussian distributed low dimensions. The design of the filter operator which can maintain the trade-off balance between exploitation and exploration is also analyzed in detail. The experimental results demonstrated the good performance of the proposed SGEF in promoting both single-objective optimization genetic algorithms and multiple-objective optimization genetic algorithms.
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