research-article

Opt4J: a modular framework for meta-heuristic optimization

Authors:

Martin Lukasiewycz,

Jürgen TeichAuthors Info & Claims

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

Pages 1723 - 1730

https://doi.org/10.1145/2001576.2001808

Published: 12 July 2011 Publication History

Abstract

This paper presents a modular framework for meta-heuristic optimization of complex optimization tasks by decomposing them into subtasks that may be designed and developed separately. Since these subtasks are generally correlated, a separate optimization is prohibited and the framework has to be capable of optimizing the subtasks concurrently. For this purpose, a distinction of genetic representation (genotype) and representation of a solution of the optimization problem (phenotype) is imposed. A compositional genotype and appropriate operators enable the separate development and testing of the optimization of subtasks by a strict decoupling. The proposed concept is implemented as open source reference OPT4J [6]. The architecture of this implementation is outlined and design decisions are discussed that enable a maximal decoupling and flexibility. A case study of a complex real-world optimization problem from the automotive domain is introduced. This case study requires the concurrent optimization of several heterogeneous aspects. Exemplary, it is shown how the proposed framework allows to efficiently optimize this complex problem by decomposing it into subtasks that are optimized concurrently.

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Cited By

Esper KWildermann STeich JNakamura YWang Y(2025)Response Range Optimization for Run-Time Requirement Enforcement on MPSoCsProceedings of the 30th Asia and South Pacific Design Automation Conference10.1145/3658617.3697552(1160-1166)Online publication date: 20-Jan-2025
https://dl.acm.org/doi/10.1145/3658617.3697552
Pătrăușanu AFlorea ANeghină MDicoiu AChiș R(2024)A Systematic Review of Multi-Objective Evolutionary Algorithms Optimization FrameworksProcesses10.3390/pr1205086912:5(869)Online publication date: 26-Apr-2024
https://doi.org/10.3390/pr12050869
Chen TLi M(2024)Adapting Multi-objectivized Software Configuration TuningProceedings of the ACM on Software Engineering10.1145/36437511:FSE(539-561)Online publication date: 12-Jul-2024
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Index Terms

Opt4J: a modular framework for meta-heuristic optimization
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction
2. Mathematics of computing
  1. Probability and statistics
    1. Probabilistic algorithms
    2. Probabilistic reasoning algorithms
      1. Markov-chain Monte Carlo methods
      2. Sequential Monte Carlo methods

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cover image ACM Conferences

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

July 2011

2140 pages

ISBN:9781450305570

DOI:10.1145/2001576

Editor:
Natalio Krasnogor
University of Nottingham, UK
,
General Chair:
Pier Luca Lanzi
Politecnico di Milano, Italy

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2011

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GECCO '11

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SIGEVO

GECCO '11: Genetic and Evolutionary Computation Conference

July 12 - 16, 2011

Dublin, Ireland

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Esper KWildermann STeich JNakamura YWang Y(2025)Response Range Optimization for Run-Time Requirement Enforcement on MPSoCsProceedings of the 30th Asia and South Pacific Design Automation Conference10.1145/3658617.3697552(1160-1166)Online publication date: 20-Jan-2025
https://dl.acm.org/doi/10.1145/3658617.3697552
Pătrăușanu AFlorea ANeghină MDicoiu AChiș R(2024)A Systematic Review of Multi-Objective Evolutionary Algorithms Optimization FrameworksProcesses10.3390/pr1205086912:5(869)Online publication date: 26-Apr-2024
https://doi.org/10.3390/pr12050869
Chen TLi M(2024)Adapting Multi-objectivized Software Configuration TuningProceedings of the ACM on Software Engineering10.1145/36437511:FSE(539-561)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3643751
Chen PChen TLi M(2024)MMO: Meta Multi-Objectivization for Software Configuration TuningIEEE Transactions on Software Engineering10.1109/TSE.2024.338891050:6(1478-1504)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1109/TSE.2024.3388910
Pang LIshibuchi HHe LShang KChen L(2024)Hypervolume-Based Cooperative Coevolution With Two Reference Points for Multiobjective OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.328739928:4(1054-1068)Online publication date: Aug-2024
https://doi.org/10.1109/TEVC.2023.3287399
Letras MFalk JTeich J(2024)Exploring Multi-Reader Buffers and Channel Placement During Dataflow Network Mapping to Heterogeneous Many-Core SystemsIEEE Access10.1109/ACCESS.2024.337507912(39748-39769)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3375079
Heidorn CSabih MMeyerhöfer NSchinabeck CTeich JHannig F(2024)Hardware-Aware Evolutionary Explainable Filter Pruning for Convolutional Neural NetworksInternational Journal of Parallel Programming10.1007/s10766-024-00760-552:1-2(40-58)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s10766-024-00760-5
Shi HGuo FChen YXu LWang R(2024)Improving BFGO with Apical Dominance-Guided Gradient Descent for Enhanced OptimizationGenetic and Evolutionary Computing10.1007/978-981-99-9412-0_14(128-137)Online publication date: 25-Jan-2024
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Gmyrek KMyszkowski PAntkiewicz MOlech Ł(2024)iMOPSE: a Comprehensive Open Source Library for Single- and Multi-objective Metaheuristic OptimizationParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70068-2_11(170-184)Online publication date: 7-Sep-2024
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Esper KWildermann STeich J(2023)Automatic Synthesis of FSMs for Enforcing Non-functional Requirements on MPSoCs Using Multi-objective Evolutionary AlgorithmsACM Transactions on Design Automation of Electronic Systems10.1145/361783228:6(1-20)Online publication date: 16-Oct-2023
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