Improving NeuroEvolution Efficiency by Surrogate Model-based Optimization with Phenotypic Distance Kernels

02/09/2019
by   Jörg Stork, et al.
0

In NeuroEvolution, the topologies of artificial neural networks are optimized with evolutionary algorithms to solve tasks in data regression, data classification, or reinforcement learning. One downside of NeuroEvolution is the large amount of necessary fitness evaluations, which might render it inefficient for tasks with expensive evaluations, such as real-time learning. For these expensive optimization tasks, surrogate model-based optimization is frequently applied as it features a good evaluation efficiency. While a combination of both procedures appears as a valuable solution, the definition of adequate distance measures for the surrogate modeling process is difficult. In this study, we will extend cartesian genetic programming of artificial neural networks by the use of surrogate model-based optimization. We propose different distance measures and test our algorithm on a replicable benchmark task. The results indicate that we can significantly increase the evaluation efficiency and that a phenotypic distance, which is based on the behavior of the associated neural networks, is most promising.

READ FULL TEXT
research
07/22/2019

Surrogate Models for Enhancing the Efficiency of Neuroevolution in Reinforcement Learning

In the last years, reinforcement learning received a lot of attention. O...
research
07/20/2018

Distance-based Kernels for Surrogate Model-based Neuroevolution

The topology optimization of artificial neural networks can be particula...
research
07/03/2018

Linear Combination of Distance Measures for Surrogate Models in Genetic Programming

Surrogate models are a well established approach to reduce the number of...
research
07/03/2018

A First Analysis of Kernels for Kriging-based Optimization in Hierarchical Search Spaces

Many real-world optimization problems require significant resources for ...
research
09/29/2020

Neural Model-based Optimization with Right-Censored Observations

In many fields of study, we only observe lower bounds on the true respon...
research
04/15/2018

Data-efficient Neuroevolution with Kernel-Based Surrogate Models

Surrogate-assistance approaches have long been used in computationally e...
research
11/30/2021

Surrogate-based optimization using an artificial neural network for a parameter identification in a 3D marine ecosystem model

Parameter identification for marine ecosystem models is important for th...

Please sign up or login with your details

Forgot password? Click here to reset