Search Results for author: Thomas Bäck

Found 78 papers, 21 papers with code

Large-scale Benchmarking of Metaphor-based Optimization Heuristics

no code implementations15 Feb 2024 Diederick Vermetten, Carola Doerr, Hao Wang, Anna V. Kononova, Thomas Bäck

The number of proposed iterative optimization heuristics is growing steadily, and with this growth, there have been many points of discussion within the wider community.

Benchmarking Experimental Design

Solving Deep Reinforcement Learning Benchmarks with Linear Policy Networks

no code implementations10 Feb 2024 Annie Wong, Jacob de Nobel, Thomas Bäck, Aske Plaat, Anna V. Kononova

Although Deep Reinforcement Learning (DRL) methods can learn effective policies for challenging problems such as Atari games and robotics tasks, algorithms are complex and training times are often long.

Atari Games reinforcement-learning

A Functional Analysis Approach to Symbolic Regression

no code implementations9 Feb 2024 Kirill Antonov, Roman Kalkreuth, Kaifeng Yang, Thomas Bäck, Niki van Stein, Anna V Kononova

The superior performance of the proposed algorithm and insights into the limitations of GP open the way for further advancing GP for SR and related areas of explainable machine learning.

Benchmarking regression +1

Shapelet-based Model-agnostic Counterfactual Local Explanations for Time Series Classification

no code implementations2 Feb 2024 Qi Huang, Wei Chen, Thomas Bäck, Niki van Stein

In this work, we propose a model-agnostic instance-based post-hoc explainability method for time series classification.

Classification counterfactual +2

Explainable Benchmarking for Iterative Optimization Heuristics

1 code implementation31 Jan 2024 Niki van Stein, Diederick Vermetten, Anna V. Kononova, Thomas Bäck

Introducing the IOH-Xplainer software framework, for analyzing and understanding the performance of various optimization algorithms and the impact of their different components and hyper-parameters.

Benchmarking Evolutionary Algorithms

MA-BBOB: A Problem Generator for Black-Box Optimization Using Affine Combinations and Shifts

no code implementations18 Dec 2023 Diederick Vermetten, Furong Ye, Thomas Bäck, Carola Doerr

Choosing a set of benchmark problems is often a key component of any empirical evaluation of iterative optimization heuristics.


On the Noise Scheduling for Generating Plausible Designs with Diffusion Models

no code implementations19 Nov 2023 Jiajie Fan, Laure Vuaille, Thomas Bäck, Hao Wang

We delve into the impact of noise schedules of diffusion models on the plausibility of the outcome: there exists a range of noise levels at which the model's performance decides the result plausibility.


MA-VAE: Multi-head Attention-based Variational Autoencoder Approach for Anomaly Detection in Multivariate Time-series Applied to Automotive Endurance Powertrain Testing

1 code implementation5 Sep 2023 Lucas Correia, Jan-Christoph Goos, Philipp Klein, Thomas Bäck, Anna V. Kononova

A clear need for automatic anomaly detection applied to automotive testing has emerged as more and more attention is paid to the data recorded and manual evaluation by humans reaches its capacity.

Anomaly Detection Time Series

Adversarial Latent Autoencoder with Self-Attention for Structural Image Synthesis

no code implementations19 Jul 2023 Jiajie Fan, Laure Vuaille, Hao Wang, Thomas Bäck

The potential of SA-ALAE is shown by generating engineering blueprints in a real automotive design task.

Image Generation

MA-BBOB: Many-Affine Combinations of BBOB Functions for Evaluating AutoML Approaches in Noiseless Numerical Black-Box Optimization Contexts

no code implementations18 Jun 2023 Diederick Vermetten, Furong Ye, Thomas Bäck, Carola Doerr

Extending a recent suggestion to generate new instances for numerical black-box optimization benchmarking by interpolating pairs of the well-established BBOB functions from the COmparing COntinuous Optimizers (COCO) platform, we propose in this work a further generalization that allows multiple affine combinations of the original instances and arbitrarily chosen locations of the global optima.

AutoML Benchmarking

Challenges of ELA-guided Function Evolution using Genetic Programming

no code implementations24 May 2023 Fu Xing Long, Diederick Vermetten, Anna V. Kononova, Roman Kalkreuth, Kaifeng Yang, Thomas Bäck, Niki van Stein

Within the optimization community, the question of how to generate new optimization problems has been gaining traction in recent years.

When to be Discrete: Analyzing Algorithm Performance on Discretized Continuous Problems

no code implementations25 Apr 2023 André Thomaser, Jacob de Nobel, Diederick Vermetten, Furong Ye, Thomas Bäck, Anna V. Kononova

In this work, we use the notion of the resolution of continuous variables to discretize problems from the continuous domain.

Modular Differential Evolution

no code implementations19 Apr 2023 Diederick Vermetten, Fabio Caraffini, Anna V. Kononova, Thomas Bäck

Although these contributions are often compared to the base algorithm, it is challenging to make fair comparisons between larger sets of algorithm variants.

DoE2Vec: Deep-learning Based Features for Exploratory Landscape Analysis

1 code implementation31 Mar 2023 Bas van Stein, Fu Xing Long, Moritz Frenzel, Peter Krause, Markus Gitterle, Thomas Bäck

We propose DoE2Vec, a variational autoencoder (VAE)-based methodology to learn optimization landscape characteristics for downstream meta-learning tasks, e. g., automated selection of optimization algorithms.

Feature Engineering Meta-Learning

Multi-surrogate Assisted Efficient Global Optimization for Discrete Problems

no code implementations13 Dec 2022 Qi Huang, Roy de Winter, Bas van Stein, Thomas Bäck, Anna V. Kononova

Decades of progress in simulation-based surrogate-assisted optimization and unprecedented growth in computational power have enabled researchers and practitioners to optimize previously intractable complex engineering problems.


Optimizing Stimulus Energy for Cochlear Implants with a Machine Learning Model of the Auditory Nerve

no code implementations14 Nov 2022 Jacob de Nobel, Anna V. Kononova, Jeroen Briaire, Johan Frijns, Thomas Bäck

In the second part of this paper, the Convolutional Neural Network surrogate model was used by an Evolutionary Algorithm to optimize the shape of the stimulus waveform in terms energy efficiency.

Saliency Can Be All You Need In Contrastive Self-Supervised Learning

no code implementations30 Oct 2022 Veysel Kocaman, Ofer M. Shir, Thomas Bäck, Ahmed Nabil Belbachir

We propose an augmentation policy for Contrastive Self-Supervised Learning (SSL) in the form of an already established Salient Image Segmentation technique entitled Global Contrast based Salient Region Detection.

Image Segmentation Segmentation +2

Deep Learning based pipeline for anomaly detection and quality enhancement in industrial binder jetting processes

no code implementations21 Sep 2022 Alexander Zeiser, Bas van Stein, Thomas Bäck

Anomaly detection describes methods of finding abnormal states, instances or data points that differ from a normal value space.

Anomaly Detection

Reinforcement Learning Assisted Recursive QAOA

1 code implementation13 Jul 2022 Yash J. Patel, Sofiene Jerbi, Thomas Bäck, Vedran Dunjko

Variational quantum algorithms such as the Quantum Approximation Optimization Algorithm (QAOA) in recent years have gained popularity as they provide the hope of using NISQ devices to tackle hard combinatorial optimization problems.

Combinatorial Optimization reinforcement-learning +1

Optimally Weighted Ensembles of Regression Models: Exact Weight Optimization and Applications

no code implementations22 Jun 2022 Patrick Echtenbruck, Martina Echtenbruck, Joost Batenburg, Thomas Bäck, Boris Naujoks, Michael Emmerich

More specifically, in this paper, a heuristic weight optimization, used in a preceding conference paper, is replaced by an exact optimization algorithm using convex quadratic programming.

Drug Discovery Model Selection +1

Quantum-Enhanced Selection Operators for Evolutionary Algorithms

no code implementations21 Jun 2022 David Von Dollen, Sheir Yarkoni, Daniel Weimer, Florian Neukart, Thomas Bäck

We benchmark these quantum-enhanced algorithms against classical algorithms over various black-box objective functions, including the OneMax function, and functions from the IOHProfiler library for black-box optimization.

Evolutionary Algorithms

Hyperparameter Importance of Quantum Neural Networks Across Small Datasets

no code implementations20 Jun 2022 Charles Moussa, Jan N. van Rijn, Thomas Bäck, Vedran Dunjko

In this domain, one of the more investigated approaches is the use of a special type of quantum circuit - a so-called quantum neural network -- to serve as a basis for a machine learning model.

BIG-bench Machine Learning Model Selection +1

Equivariant quantum circuits for learning on weighted graphs

2 code implementations12 May 2022 Andrea Skolik, Michele Cattelan, Sheir Yarkoni, Thomas Bäck, Vedran Dunjko

When training a parametrized quantum circuit in this setting to solve a specific problem, the choice of ansatz is one of the most important factors that determines the trainability and performance of the algorithm.

Combinatorial Optimization Quantum Machine Learning

Analyzing the Impact of Undersampling on the Benchmarking and Configuration of Evolutionary Algorithms

no code implementations20 Apr 2022 Diederick Vermetten, Hao Wang, Manuel López-Ibañez, Carola Doerr, Thomas Bäck

Particularly, we show that the number of runs used in many benchmarking studies, e. g., the default value of 15 suggested by the COCO environment, can be insufficient to reliably rank algorithms on well-known numerical optimization benchmarks.

Benchmarking Evolutionary Algorithms

Switching between Numerical Black-box Optimization Algorithms with Warm-starting Policies

no code implementations13 Apr 2022 Dominik Schröder, Diederick Vermetten, Hao Wang, Carola Doerr, Thomas Bäck

In this work, we build on the recent study of Vermetten et al. [GECCO 2020], who presented a data-driven approach to investigate promising switches between pairs of algorithms for numerical black-box optimization.

Explainable Artificial Intelligence for Exhaust Gas Temperature of Turbofan Engines

no code implementations24 Mar 2022 Marios Kefalas, Juan de Santiago Rojo Jr., Asteris Apostolidis, Dirk van den Herik, Bas van Stein, Thomas Bäck

Data-driven modeling is an imperative tool in various industrial applications, including many applications in the sectors of aeronautics and commercial aviation.

Explainable artificial intelligence Symbolic Regression

Non-Elitist Selection Can Improve the Performance of Irace

1 code implementation17 Mar 2022 Furong Ye, Diederick L. Vermetten, Carola Doerr, Thomas Bäck

In addition, the obtained results indicate that non-elitist can obtain diverse algorithm configurations, which encourages us to explore a wider range of solutions to understand the behavior of algorithms.

Bayesian Optimization Evolutionary Algorithms

IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics

1 code implementation7 Nov 2021 Jacob de Nobel, Furong Ye, Diederick Vermetten, Hao Wang, Carola Doerr, Thomas Bäck

IOHexperimenter can be used as a stand-alone tool or as part of a benchmarking pipeline that uses other components of IOHprofiler such as IOHanalyzer, the module for interactive performance analysis and visualization.

Bayesian Optimization Benchmarking

Lifelong Computing

no code implementations19 Aug 2021 Danny Weyns, Thomas Bäck, Renè Vidal, Xin Yao, Ahmed Nabil Belbachir

When detecting anomalies, novelties, new goals or constraints, a lifelong computing system activates an evolutionary self-learning engine that runs online experiments to determine how the computing-learning system needs to evolve to deal with the changes, thereby changing its architecture and integrating new computing elements from computing warehouses as needed.


Automated Configuration of Genetic Algorithms by Tuning for Anytime Performance

1 code implementation11 Jun 2021 Furong Ye, Carola Doerr, Hao Wang, Thomas Bäck

Finding the best configuration of algorithms' hyperparameters for a given optimization problem is an important task in evolutionary computation.

Expressivity of Parameterized and Data-driven Representations in Quality Diversity Search

1 code implementation10 May 2021 Alexander Hagg, Sebastian Berns, Alexander Asteroth, Simon Colton, Thomas Bäck

We consider multi-solution optimization and generative models for the generation of diverse artifacts and the discovery of novel solutions.

Is there Anisotropy in Structural Bias?

no code implementations10 May 2021 Diederick Vermetten, Anna V. Kononova, Fabio Caraffini, Hao Wang, Thomas Bäck

We find that anisotropy is very rare, and even in cases where it is present, there are clear tests for SB which do not rely on any assumptions of isotropy, so we can safely expand the suite of SB tests to encompass these kinds of deficiencies not found by the original tests.

Designing Air Flow with Surrogate-assisted Phenotypic Niching

no code implementations10 May 2021 Alexander Hagg, Dominik Wilde, Alexander Asteroth, Thomas Bäck

In complex, expensive optimization domains we often narrowly focus on finding high performing solutions, instead of expanding our understanding of the domain itself.

Explorative Data Analysis of Time Series based AlgorithmFeatures of CMA-ES Variants

no code implementations16 Apr 2021 Jacob de Nobel, Hao Wang, Thomas Bäck

From our analysis, we saw that the features can classify the CMA-ES variants, or the function groups decently, and show a potential for predicting the performance of those variants.

Clustering feature selection +3

Quantum-Assisted Feature Selection for Vehicle Price Prediction Modeling

no code implementations8 Apr 2021 David Von Dollen, Florian Neukart, Daniel Weimer, Thomas Bäck

Within machine learning model evaluation regimes, feature selection is a technique to reduce model complexity and improve model performance in regards to generalization, model fit, and accuracy of prediction.

feature selection

Robust subgroup discovery

2 code implementations25 Mar 2021 Hugo Manuel Proença, Peter Grünwald, Thomas Bäck, Matthijs van Leeuwen

This novel model class allows us to formalise the problem of optimal robust subgroup discovery using the Minimum Description Length (MDL) principle, where we resort to optimal Normalised Maximum Likelihood and Bayesian encodings for nominal and numeric targets, respectively.

Subgroup Discovery

PREPRINT: Comparison of deep learning and hand crafted features for mining simulation data

no code implementations11 Mar 2021 Theodoros Georgiou, Sebastian Schmitt, Thomas Bäck, Nan Pu, Wei Chen, Michael Lew

The output of such simulations, in particular the calculated flow fields, are usually very complex and hard to interpret for realistic three-dimensional real-world applications, especially if time-dependent simulations are investigated.

Tuning as a Means of Assessing the Benefits of New Ideas in Interplay with Existing Algorithmic Modules

1 code implementation25 Feb 2021 Jacob de Nobel, Diederick Vermetten, Hao Wang, Carola Doerr, Thomas Bäck

However, when introducing a new component into an existing algorithm, assessing its potential benefits is a challenging task.

Leveraging Benchmarking Data for Informed One-Shot Dynamic Algorithm Selection

no code implementations12 Feb 2021 Furong Ye, Carola Doerr, Thomas Bäck

What complicates this decision further is that different algorithms may be best suited for different stages of the optimization process.

AutoML Benchmarking +1

Learning adaptive differential evolution algorithm from optimization experiences by policy gradient

no code implementations6 Feb 2021 Jianyong Sun, Xin Liu, Thomas Bäck, Zongben Xu

A reinforcement learning algorithm, named policy gradient, is applied to learn an agent (i. e. parameter controller) that can provide the control parameters of a proposed differential evolution adaptively during the search procedure.

Evolutionary Algorithms

Improving Model Accuracy for Imbalanced Image Classification Tasks by Adding a Final Batch Normalization Layer: An Empirical Study

no code implementations12 Nov 2020 Veysel Kocaman, Ofer M. Shir, Thomas Bäck

We empirically observe that the initial F1 test score jumps from 0. 29 to 0. 95 for the minority class upon adding a final Batch Normalization (BN) layer just before the output layer in VGG19.

Image Classification imbalanced classification +1

Neural Network Design: Learning from Neural Architecture Search

1 code implementation1 Nov 2020 Bas van Stein, Hao Wang, Thomas Bäck

Neural Architecture Search (NAS) aims to optimize deep neural networks' architecture for better accuracy or smaller computational cost and has recently gained more research interests.

Benchmarking Image Classification +1

IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics

3 code implementations8 Jul 2020 Hao Wang, Diederick Vermetten, Furong Ye, Carola Doerr, Thomas Bäck

An R programming interface is provided for users preferring to have a finer control over the implemented functionalities.

Bayesian Optimization Benchmarking +1

A Modular Hybridization of Particle Swarm Optimization and Differential Evolution

1 code implementation21 Jun 2020 Rick Boks, Hao Wang, Thomas Bäck

In swarm intelligence, Particle Swarm Optimization (PSO) and Differential Evolution (DE) have been successfully applied in many optimization tasks, and a large number of variants, where novel algorithm operators or components are implemented, has been introduced to boost the empirical performance.

Discovering outstanding subgroup lists for numeric targets using MDL

3 code implementations16 Jun 2020 Hugo M. Proença, Peter Grünwald, Thomas Bäck, Matthijs van Leeuwen

We propose a dispersion-aware problem formulation for subgroup set discovery that is based on the minimum description length (MDL) principle and subgroup lists.

Attribute Subgroup Discovery

Towards Dynamic Algorithm Selection for Numerical Black-Box Optimization: Investigating BBOB as a Use Case

1 code implementation11 Jun 2020 Diederick Vermetten, Hao Wang, Carola Doerr, Thomas Bäck

One of the most challenging problems in evolutionary computation is to select from its family of diverse solvers one that performs well on a given problem.

Benchmarking a $(μ+λ)$ Genetic Algorithm with Configurable Crossover Probability

no code implementations10 Jun 2020 Furong Ye, Hao Wang, Carola Doerr, Thomas Bäck

Moreover, we observe that the ``fast'' mutation scheme with its are power-law distributed mutation strengths outperforms standard bit mutation on complex optimization tasks when it is combined with crossover, but performs worse in the absence of crossover.


A Novel Column Generation Heuristic for Airline Crew Pairing Optimization with Large-scale Complex Flight Networks

no code implementations18 May 2020 Divyam Aggarwal, Dhish Kumar Saxena, Saaju Pualose, Thomas Bäck, Michael Emmerich

Crew Pairing Optimization (CPO) is critical for an airlines' business viability, given that the crew operating cost is second only to the fuel cost.

Combinatorial Optimization

Differential evolution outside the box

no code implementations22 Apr 2020 Anna V. Kononova, Fabio Caraffini, Thomas Bäck

A wide range of popular Differential Evolution configurations is considered in this study.

Improving Many-Objective Evolutionary Algorithms by Means of Edge-Rotated Cones

no code implementations15 Apr 2020 Yali Wang, André Deutz, Thomas Bäck, Michael Emmerich

Given a point in $m$-dimensional objective space, any $\varepsilon$-ball of a point can be partitioned into the incomparable, the dominated and dominating region.

Evolutionary Algorithms

A Tailored NSGA-III Instantiation for Flexible Job Shop Scheduling

no code implementations14 Apr 2020 Yali Wang, Bas van Stein, Michael T. M. Emmerich, Thomas Bäck

A customized multi-objective evolutionary algorithm (MOEA) is proposed for the multi-objective flexible job shop scheduling problem (FJSP).

Job Shop Scheduling Scheduling

On Initializing Airline Crew Pairing Optimization for Large-scale Complex Flight Networks

no code implementations15 Mar 2020 Divyam Aggarwal, Dhish Kumar Saxena, Thomas Bäck, Michael Emmerich

Even generating an initial feasible solution (IFS: a manageable set of legal pairings covering all flights), which could be subsequently optimized is a difficult (NP-complete) problem.

Benchmarking Discrete Optimization Heuristics with IOHprofiler

no code implementations19 Dec 2019 Carola Doerr, Furong Ye, Naama Horesh, Hao Wang, Ofer M. Shir, Thomas Bäck

Automated benchmarking environments aim to support researchers in understanding how different algorithms perform on different types of optimization problems.


Sequential vs. Integrated Algorithm Selection and Configuration: A Case Study for the Modular CMA-ES

no code implementations12 Dec 2019 Diederick Vermetten, Hao Wang, Carola Doerr, Thomas Bäck

In this work we compare sequential and integrated algorithm selection and configuration approaches for the case of selecting and tuning the best out of 4608 variants of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) tested on the Black Box Optimization Benchmark (BBOB) suite.

Hyperparameter Optimization

Modeling User Selection in Quality Diversity

no code implementations16 Jul 2019 Alexander Hagg, Alexander Asteroth, Thomas Bäck

The initial phase in real world engineering optimization and design is a process of discovery in which not all requirements can be made in advance, or are hard to formalize.

Efficient Computation of Expected Hypervolume Improvement Using Box Decomposition Algorithms

no code implementations26 Apr 2019 Kaifeng Yang, Michael Emmerich, André Deutz, Thomas Bäck

In this paper, an efficient algorithm for the computation of the exact EHVI for a generic case is proposed.

SACOBRA with Online Whitening for Solving Optimization Problems with High Conditioning

no code implementations17 Apr 2019 Samineh Bagheri, Wolfgang Konen, Thomas Bäck

We show on a set of high-conditioning functions that online whitening tackles SACOBRA's early stagnation issue and reduces the optimization error by a factor between 10 to 1e12 as compared to the plain SACOBRA, though it imposes many extra function evaluations.

Vocal Bursts Intensity Prediction

Online Selection of CMA-ES Variants

no code implementations16 Apr 2019 Diederick Vermetten, Sander van Rijn, Thomas Bäck, Carola Doerr

An analysis of module activation indicates which modules are most crucial for the different phases of optimizing each of the 24 benchmark problems.

Interpolating Local and Global Search by Controlling the Variance of Standard Bit Mutation

no code implementations17 Jan 2019 Furong Ye, Carola Doerr, Thomas Bäck

We introduce in this work a simple way to interpolate between the random global search of EAs and their deterministic counterparts which sample from a fixed radius only.

Evolutionary Algorithms

IOHprofiler: A Benchmarking and Profiling Tool for Iterative Optimization Heuristics

5 code implementations11 Oct 2018 Carola Doerr, Hao Wang, Furong Ye, Sander van Rijn, Thomas Bäck

Given as input algorithms and problems written in C or Python, it provides as output a statistical evaluation of the algorithms' performance by means of the distribution on the fixed-target running time and the fixed-budget function values.


Automatic Configuration of Deep Neural Networks with EGO

1 code implementation10 Oct 2018 Bas van Stein, Hao Wang, Thomas Bäck

In this paper an Efficient Global Optimization (EGO) algorithm is adapted to automatically optimize and configure convolutional neural network architectures.

Data Augmentation Image Classification

Towards a Theory-Guided Benchmarking Suite for Discrete Black-Box Optimization Heuristics: Profiling $(1+λ)$ EA Variants on OneMax and LeadingOnes

no code implementations17 Aug 2018 Carola Doerr, Furong Ye, Sander van Rijn, Hao Wang, Thomas Bäck

Marking an important step towards filling this gap, we adjust the COCO software to pseudo-Boolean optimization problems, and obtain from this a benchmarking environment that allows a fine-grained empirical analysis of discrete black-box heuristics.

Benchmarking Evolutionary Algorithms +1

Prototype Discovery using Quality-Diversity

no code implementations25 Jul 2018 Alexander Hagg, Alexander Asteroth, Thomas Bäck

An iterative computer-aided ideation procedure is introduced, building on recent quality-diversity algorithms, which search for diverse as well as high-performing solutions.

Dimensionality Reduction

Artificial Intelligence and Data Science in the Automotive Industry

no code implementations6 Sep 2017 Martin Hofmann, Florian Neukart, Thomas Bäck

Data science and machine learning are the key technologies when it comes to the processes and products with automatic learning and optimization to be used in the automotive industry of the future.

BIG-bench Machine Learning Marketing

Cluster-based Kriging Approximation Algorithms for Complexity Reduction

no code implementations4 Feb 2017 Bas van Stein, Hao Wang, Wojtek Kowalczyk, Michael Emmerich, Thomas Bäck

In addition, four Kriging approximation algorithms are proposed as candidate algorithms within the new framework.


Local Subspace-Based Outlier Detection using Global Neighbourhoods

1 code implementation1 Nov 2016 Bas van Stein, Matthijs van Leeuwen, Thomas Bäck

In highly complex and high-dimensional data, however, existing methods are likely to overlook important outliers because they do not explicitly take into account that the data is often a mixture distribution of multiple components.

Fraud Detection Outlier Detection

Evolving the Structure of Evolution Strategies

no code implementations17 Oct 2016 Sander van Rijn, Hao Wang, Matthijs van Leeuwen, Thomas Bäck

Various variants of the well known Covariance Matrix Adaptation Evolution Strategy (CMA-ES) have been proposed recently, which improve the empirical performance of the original algorithm by structural modifications.

Solving the G-problems in less than 500 iterations: Improved efficient constrained optimization by surrogate modeling and adaptive parameter control

no code implementations31 Dec 2015 Samineh Bagheri, Wolfgang Konen, Michael Emmerich, Thomas Bäck

We analyze the importance of the several new elements in SACOBRA and find that each element of SACOBRA plays a role to boost up the overall optimization performance.

Multiobjective Optimization of Classifiers by Means of 3-D Convex Hull Based Evolutionary Algorithm

no code implementations18 Dec 2014 Jiaqi Zhao, Vitor Basto Fernandes, Licheng Jiao, Iryna Yevseyeva, Asep Maulana, Rui Li, Thomas Bäck, Michael T. M. Emmerich

The design of the algorithm proposed in this paper is inspired by indicator-based evolutionary algorithms, where first a performance indicator for a solution set is established and then a selection operator is designed that complies with the performance indicator.

Binary Classification Classification +5

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