Informed operators: Speeding up genetic-algorithm-based design optimization using reduced models

In this paper we describe a method for improving genetic-algorithm-based optimization using informed genetic operators. The idea is to make the genetic operators such as mutation and crossover more informed using reduced models. In every place where a random choice is made, for example when a point is mutated, instead of generating just one random mutation we generate several, rank them using a reduced model, then take the best to be the result of the mutation. The proposed method is particularly suitable for search spaces with expensive evaluation functions, such as arise in engineering design. Empirical results in several engineering design domains demonstrate that the proposed method can significantly speed up the GA optimizer.

• Type: Conference
• Year: 2000
• Where: GECCO
• Authors: Khaled Rasheed, Haym Hirsh
• Keywords: Engineering Design, GECCO 2000, Genetic Operators, Informed Genetic Operators, Optimization