Novelty and interestingness measures for design-space exploration

Authors:
Edgar Reehuis;Markus Olhofer;Michael Emmerich;Bernhard Sendhoff;Thomas Bäck
Affiliations:
LIACS, Leiden University, Leiden, Netherlands;Honda Research Institute Europe, Offenbach, Germany;LIACS, Leiden University, Leiden, Netherlands;Honda Research Institute Europe, Offenbach, Germany;LIACS, Leiden University, Leiden, Netherlands
Venue:
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Year:
2013

Citing 10
Cited 0

Efficient Global Optimization of Expensive Black-Box Functions

Journal of Global Optimization
What''s interesting?

What''s interesting?
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)

Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Adaptive niche radii and niche shapes approaches for niching with the cma-es

Evolutionary Computation
Generalizing surrogate-assisted evolutionary computation

IEEE Transactions on Evolutionary Computation
Mixed-integer evolution strategy using multiobjective selection applied to warehouse design optimization

Proceedings of the 12th annual conference on Genetic and evolutionary computation
Towards directed open-ended search by a novelty guided evolution strategy

PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Abandoning objectives: Evolution through the search for novelty alone

Evolutionary Computation
Intrinsic Motivation Systems for Autonomous Mental Development

IEEE Transactions on Evolutionary Computation
Active learning of inverse models with intrinsically motivated goal exploration in robots

Robotics and Autonomous Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Measures of novelty and interestingness are frequently encountered in the context of developmental robotics, being derived from human psychology. This work addresses these measures from the viewpoint of enhancing design-space exploration in black-box optimization. We provide a unifying notational and naming scheme with the intent of facilitating comparison, implementation, and application in the domain of design optimization. Initial analysis shows a promising interestingness measure for being tried on real-world design problems.