Towards Self-Learning Model-Based Evolutionary Algorithms

Towards Self-Learning Model-Based Evolutionary Algorithms

Meulman, Erik (TU Delft Electrical Engineering, Mathematics and Computer Science)

Bosman, Peter (mentor)
Aardal, Karen (graduation committee)
Bierkens, Joris (graduation committee)

Delft University of Technology

Applied Mathematics

2019-04-17

Model-based evolutionary algorithms (MBEAs) are praised for their broad applicability to black-box optimization problems. In practical applications however, they are mostly used to repeatedly optimize different instances of a single problem class, a setting in which specialized algorithms generally perform better. In this paper, we introduce the concept of a new type of MBEA that can automatically specialize its behavior to a given problem class using tabula rasa self-learning. For this, reinforcement learning (RL) is a naturally fitting paradigm. A proof-of-principle framework, called SL-ENDA, based on estimation of normal distribution algorithms in combination with reinforcement learning is defined. SL-ENDA uses an RL-agent to decide upon the next population mean while approaching the rest of the algorithm as the environment. A comparison of SL-ENDA to AMaLGaM and CMA-ES on unimodal noiseless functions shows mostly comparable performance and scalability to the broadly used and carefully manually crafted algorithms. This result, in combination with the inherent potential of self-learning model-based evolutionary algorithms with regard to specialization, opens the door to a new research direction with great potential impact on the field of model-based evolutionary algorithms.

Estimation of distribution algorithms
Machine Learning
Reinforcement Learning (RL)
Black-box optimization

http://resolver.tudelft.nl/uuid:d7e9fdb1-7ced-43bb-b3ab-c4888fcc2482

Student theses

master thesis

© 2019 Erik Meulman