Update-based evolution control: A new fitness approximation method for evolutionary algorithms

Abstract

Evolutionary algorithms are robust optimization methods that have been used in many engineering applications. However, real-world fitness evaluations can be computationally expensive, so it may be necessary to estimate the fitness with an approximate model. This article reviews design and analysis of computer experiments (DACE) as an approximation method that combines a global polynomial with a local Gaussian model to estimate continuous fitness functions. The article incorporates DACE in various evolutionary algorithms, to test unconstrained and constrained benchmarks, both with and without fitness function evaluation noise. The article also introduces a new evolution control strategy called update-based control that estimates the fitness of certain individuals of each generation based on the exact fitness values of other individuals during that same generation. The results show that update-based evolution control outperforms other strategies on noise-free, noisy, constrained and unconstrained benchmarks. The results also show that update-based evolution control can compensate for fitness evaluation noise.

Keywords:

• evolutionary algorithm
• fitness function approximation
• design and analysis of computer experiments (DACE)
• noisy optimization
• constrained optimization

Additional information

Funding

This work is based on work supported by the National Science Foundation [grant no. 0826124], the National Natural Science Foundation of China [grant nos 61305078, 61074032 and 61179041] and the Shaoxing City Public Technology Applied Research Project [grant no. 2013B70004].