Simplifying Problem Definitions in the HeuristicLab Optimization Environment

Andreas Scheibenpflug; Andreas Beham; Michael Kommenda; Johannes Karder; Stefan Wagner; Michael Affenzeller

doi:10.1145/2739482.2768463

Simplifying Problem Definitions in the HeuristicLab Optimization Environment

Andreas Scheibenpflug
, Andreas Beham
, Michael Kommenda
, Johannes Karder
, Stefan Wagner
, Michael Affenzeller

Institute of Formal Models and Verification

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Abstract

Software frameworks for metaheuristic optimization take the burden off researchers and practitioners to start from scratch and implement their own algorithms and problems. One such framework is HeuristicLab. While it allows using existing, already implemented algorithms and problems comfortably and provides an extensive range of tools for analyzing results, it lacks an easy to use programming interface for adding new problems. As implementing new problems is a common task, an improved and simpler problem definition interface has been created. Besides giving an overview of the implementation, we also show examples of problems built using this new interface. Additionally, we compare the new approach to three other metaheuristic frameworks. This is done by analyzing the source code of the OneMax problem implemented in each framework and comparing the resulting lines of code with previous works.

Original language	English
Title of host publication	Companion Publication of the 2015 Genetic and Evolutionary Computation Conference, GECCO'15
Editors	Sara Silva
Pages	1101-1108
Number of pages	8
ISBN (Electronic)	9781450334884
DOIs	https://doi.org/10.1145/2739482.2768463
Publication status	Published - 11 Jul 2015

Fields of science

102 Computer Sciences
603109 Logic
202006 Computer hardware

JKU Focus areas

Computation in Informatics and Mathematics

Access to Document

10.1145/2739482.2768463

Cite this

@inproceedings{d45028c384f9404197821835a6527928,

title = "Simplifying Problem Definitions in the HeuristicLab Optimization Environment",

abstract = "Software frameworks for metaheuristic optimization take the burden off researchers and practitioners to start from scratch and implement their own algorithms and problems. One such framework is HeuristicLab. While it allows using existing, already implemented algorithms and problems comfortably and provides an extensive range of tools for analyzing results, it lacks an easy to use programming interface for adding new problems. As implementing new problems is a common task, an improved and simpler problem definition interface has been created. Besides giving an overview of the implementation, we also show examples of problems built using this new interface. Additionally, we compare the new approach to three other metaheuristic frameworks. This is done by analyzing the source code of the OneMax problem implemented in each framework and comparing the resulting lines of code with previous works.",

author = "Andreas Scheibenpflug and Andreas Beham and Michael Kommenda and Johannes Karder and Stefan Wagner and Michael Affenzeller",

year = "2015",

month = jul,

day = "11",

doi = "10.1145/2739482.2768463",

language = "English",

pages = "1101--1108",

editor = "Sara Silva",

booktitle = "Companion Publication of the 2015 Genetic and Evolutionary Computation Conference, GECCO'15",

}

Simplifying Problem Definitions in the HeuristicLab Optimization Environment. / Scheibenpflug, Andreas; Beham, Andreas; Kommenda, Michael et al.
Companion Publication of the 2015 Genetic and Evolutionary Computation Conference, GECCO'15. ed. / Sara Silva. 2015. p. 1101-1108.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

TY - GEN

T1 - Simplifying Problem Definitions in the HeuristicLab Optimization Environment

AU - Scheibenpflug, Andreas

AU - Beham, Andreas

AU - Kommenda, Michael

AU - Karder, Johannes

AU - Wagner, Stefan

AU - Affenzeller, Michael

PY - 2015/7/11

Y1 - 2015/7/11

N2 - Software frameworks for metaheuristic optimization take the burden off researchers and practitioners to start from scratch and implement their own algorithms and problems. One such framework is HeuristicLab. While it allows using existing, already implemented algorithms and problems comfortably and provides an extensive range of tools for analyzing results, it lacks an easy to use programming interface for adding new problems. As implementing new problems is a common task, an improved and simpler problem definition interface has been created. Besides giving an overview of the implementation, we also show examples of problems built using this new interface. Additionally, we compare the new approach to three other metaheuristic frameworks. This is done by analyzing the source code of the OneMax problem implemented in each framework and comparing the resulting lines of code with previous works.

AB - Software frameworks for metaheuristic optimization take the burden off researchers and practitioners to start from scratch and implement their own algorithms and problems. One such framework is HeuristicLab. While it allows using existing, already implemented algorithms and problems comfortably and provides an extensive range of tools for analyzing results, it lacks an easy to use programming interface for adding new problems. As implementing new problems is a common task, an improved and simpler problem definition interface has been created. Besides giving an overview of the implementation, we also show examples of problems built using this new interface. Additionally, we compare the new approach to three other metaheuristic frameworks. This is done by analyzing the source code of the OneMax problem implemented in each framework and comparing the resulting lines of code with previous works.

UR - https://www.scopus.com/pages/publications/84959420477

U2 - 10.1145/2739482.2768463

DO - 10.1145/2739482.2768463

M3 - Conference proceedings

SP - 1101

EP - 1108

BT - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference, GECCO'15

A2 - Silva, Sara

ER -

Simplifying Problem Definitions in the HeuristicLab Optimization Environment

Abstract

Fields of science

JKU Focus areas

Access to Document

Other files and links

Cite this