4.6 Review

A Literature Survey on Offline Automatic Algorithm Configuration

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Article Computer Science, Artificial Intelligence

An automatic multi-objective evolutionary algorithm for the hybrid flowshop scheduling problem with consistent sublots

Biao Zhang et al.

Summary: This paper studies a multi-objective hybrid flowshop scheduling problem with consistent sublots and proposes an automated algorithm design method to optimize a multi-objective evolutionary algorithm. By determining the combinations of numerical and categorical parameters, a trade-off between two conflicting objectives is achieved, and computational results demonstrate the superiority of the automated algorithm over other methods.

KNOWLEDGE-BASED SYSTEMS (2022)

Article Computer Science, Artificial Intelligence

Effective collaborative strategies to setup tuners

Elizabeth Montero et al.

SOFT COMPUTING (2020)

Article Computer Science, Artificial Intelligence

A Survey of Automatic Parameter Tuning Methods for Metaheuristics

Changwu Huang et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2020)

Proceedings Paper Computer Science, Artificial Intelligence

Golden Parameter Search Exploiting Structure to Quickly Configure Parameters in Parallel

Yasha Pushak et al.

GECCO'20: PROCEEDINGS OF THE 2020 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (2020)

Article Computer Science, Artificial Intelligence

Pitfalls and Best Practices in Algorithm Configuration

Katharina Eggensperger et al.

JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH (2019)

Article Computer Science, Artificial Intelligence

Tuning metaheuristics by sequential optimisation of regression models

Athila R. Trindade et al.

APPLIED SOFT COMPUTING (2019)

Article Computer Science, Artificial Intelligence

Automatic Configuration of Multi-Objective Local Search Algorithms for Permutation Problems

Aymeric Blot et al.

EVOLUTIONARY COMPUTATION (2019)

Article Computer Science, Artificial Intelligence

Efficient benchmarking of algorithm configurators via model-based surrogates

Katharina Eggensperger et al.

MACHINE LEARNING (2018)

Article Operations Research & Management Science

Effect of transformations of numerical parameters in automatic algorithm configuration

Alberto Franzin et al.

OPTIMIZATION LETTERS (2018)

Review Automation & Control Systems

Tuners review: How crucial are set-up values to find effective parameter values?

Elizabeth Montero et al.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2018)

Article Computer Science, Artificial Intelligence

MOTA: A Many-Objective Tuning Algorithm Specialized for Tuning under Multiple Objective Function Evaluation Budgets

Antoine S. Dymond et al.

EVOLUTIONARY COMPUTATION (2017)

Proceedings Paper Computer Science, Artificial Intelligence

An Experimental Study of Adaptive Capping in irace

Leslie Perez Caceres et al.

LEARNING AND INTELLIGENT OPTIMIZATION (LION 11 2017) (2017)

Article Computer Science, Artificial Intelligence

The Configurable SAT Solver Challenge (CSSC)

Frank Hutter et al.

ARTIFICIAL INTELLIGENCE (2017)

Proceedings Paper Computer Science, Artificial Intelligence

Configuring irace using surrogate configuration benchmarks

Nguyen Dang et al.

PROCEEDINGS OF THE 2017 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'17) (2017)

Proceedings Paper Computer Science, Artificial Intelligence

Evaluating random forest models for irace

Leslie Perez Caceres et al.

PROCEEDINGS OF THE 2017 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION (GECCO'17 COMPANION) (2017)

Article Computer Science, Artificial Intelligence

MaxSAT by improved instance-specific algorithm configuration

Carlos Ansotegui et al.

ARTIFICIAL INTELLIGENCE (2016)

Article Computer Science, Information Systems

Parameter tuning with Chess Rating System (CRS-Tuning) for meta-heuristic algorithms

Niki Vecek et al.

INFORMATION SCIENCES (2016)

Article Automation & Control Systems

Multi-Objective Model Selection via Racing

Tiantian Zhang et al.

IEEE TRANSACTIONS ON CYBERNETICS (2016)

Proceedings Paper Computer Science, Artificial Intelligence

MO-ParamILS: A Multi-objective Automatic Algorithm Configuration Framework

Aymeric Blot et al.

LEARNING AND INTELLIGENT OPTIMIZATION (LION 10) (2016)

Article Computer Science, Artificial Intelligence

Performance evaluation of automatically tuned continuous optimizers on different benchmark sets

Tianjun Liao et al.

APPLIED SOFT COMPUTING (2015)

Article Computer Science, Artificial Intelligence

Tuning Optimization Algorithms Under Multiple Objective Function Evaluation Budgets

Antoine S. Dymond et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2015)

Article Computer Science, Artificial Intelligence

AUTOFOLIO: An Automatically Configured Algorithm Selector

Marius Lindauer et al.

JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH (2015)

Proceedings Paper Computer Science, Artificial Intelligence

SPRINT Multi-Objective Model Racing

Tiantian Zhang et al.

GECCO'15: PROCEEDINGS OF THE 2015 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (2015)

Article Computer Science, Artificial Intelligence

A beginner's guide to tuning methods

Elizabeth Montero et al.

APPLIED SOFT COMPUTING (2014)

Article Computer Science, Artificial Intelligence

A filtering method for algorithm configuration based on consistency techniques

Ignacio Araya et al.

KNOWLEDGE-BASED SYSTEMS (2014)

Article Computer Science, Software Engineering

Optimization of algorithms with OPAL

Charles Audet et al.

MATHEMATICAL PROGRAMMING COMPUTATION (2014)

Proceedings Paper Computer Science, Artificial Intelligence

Analysis of Evolutionary Algorithms using Multi-Objective Parameter Tuning

Roberto Ugolotti et al.

GECCO'14: PROCEEDINGS OF THE 2014 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (2014)

Article Operations Research & Management Science

Efficient use of parallelism in algorithmic parameter optimization applications

C. Audet et al.

OPTIMIZATION LETTERS (2013)

Article Computer Science, Interdisciplinary Applications

Using fuzzy logic to tune an evolutionary algorithm for dynamic optimization of chemical processes

Q. T. Pham

COMPUTERS & CHEMICAL ENGINEERING (2012)

Article Computer Science, Artificial Intelligence

Continuous optimization algorithms for tuning real and integer parameters of swarm intelligence algorithms

Zhi Yuan et al.

SWARM INTELLIGENCE (2012)

Article Computer Science, Artificial Intelligence

Parameter tuning for configuring and analyzing evolutionary algorithms

A. E. Eiben et al.

SWARM AND EVOLUTIONARY COMPUTATION (2011)

Article Computer Science, Artificial Intelligence

ParamILS: An Automatic Algorithm Configuration Framework

Frank Hutter et al.

JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH (2009)

Article Operations Research & Management Science

Global optimization of stochastic black-box systems via sequential kriging meta-models

D Huang et al.

JOURNAL OF GLOBAL OPTIMIZATION (2006)

Article Mathematics, Applied

Finding optimal algorithmic parameters using derivative-free optimization

Charles Audet et al.

SIAM JOURNAL ON OPTIMIZATION (2006)

Article Management

Fine-tuning of algorithms using fractional experimental designs and local search

B Adenso-Diaz et al.

OPERATIONS RESEARCH (2006)

Article Computer Science, Artificial Intelligence

A fast and elitist multiobjective genetic algorithm: NSGA-II

K Deb et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2002)

Article Operations Research & Management Science

A survey of optimization by building and using probabilistic models

M Pelikan et al.

COMPUTATIONAL OPTIMIZATION AND APPLICATIONS (2002)

Article Computer Science, Artificial Intelligence

Completely derandomized self-adaptation in evolution strategies

N Hansen et al.

EVOLUTIONARY COMPUTATION (2001)

Article Computer Science, Artificial Intelligence

Using experimental design to find effective parameter settings for heuristics

SP Coy et al.

JOURNAL OF HEURISTICS (2001)