Large-Scale Evolutionary Strategy Based on Gradient Approximation

For large-scale optimization, CMA-ES has the disadvantages of high complexity and premature stagnation. An improved CMA-ES algorithm called GI-ES was proposed in this paper. For the problem of high complexity, the method in this paper replaces the calculation of a covariance matrix with the modeling of expected fitting degrees for a given covariance matrix. At the same time, to solve the problem of premature stagnation, this paper replaces the historical information of elite individuals with the historical information of all individuals. The information can be seen as approximate gradients. The parameters of the next generation of individuals are generated based on the approximate gradients. The experimental results were tested using CEC 2010 and CEC2013 LSGO benchmark test suite, and the experimental results verified the effectiveness of the algorithm on a number of different tasks.

Automated summary

An improved CMA-ES algorithm called GI-ES was proposed to address the issues of high complexity and premature stagnation in large-scale optimization. The method models expected fitting degrees and uses historical information of all individuals to generate approximate gradients for the next generation of individuals. Experimental results confirmed the effectiveness of the algorithm on various tasks.