---
title: "Approximation-Guided Evolutionary Multi-Objective Optimization"
authors:
  - "Karl Bringmann"
  - "Tobias Friedrich"
  - "Frank Neumann"
  - "Markus Wagner"
doi: "10.5591/978-1-57735-516-8/IJCAI11-204"
url: "https://ijcai.org/proceedings/2011/204"
pdf: "https://ijcai.org/proceedings/2011/0204.pdf"
venue: "Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence"
year: 2011
pages: "1198-1202"
track: ""
keywords:
---

# Approximation-Guided Evolutionary Multi-Objective Optimization

Karl Bringmann, Tobias Friedrich, Frank Neumann, Markus Wagner

*Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence*, pages 1198-1202. DOI: [10.5591/978-1-57735-516-8/IJCAI11-204](https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-204)

## Abstract

Multi-objective optimization problems arise frequently in applications but can often only be solved approximately by heuristic approaches. Evolutionary algorithms have been widely used to tackle multi-objective problems. These algorithms use different measures to ensure diversity in the objective space but are not guided by a formal notion of approximation. We present a new framework of an evolutionary algorithm for multi-objective optimization that allows to work with a formal notion of approximation. Our experimental results show that our approach outperforms state-of-the-art evolutionary algorithms in terms of the quality of the approximation that is obtained in particular for problems with many objectives.