Performing simulations with a realistic biophysical auditory nerve fiber model can be very time-consuming, due to the complex nature of the calculations involved. Here, a surrogate (approximate)... Show morePerforming simulations with a realistic biophysical auditory nerve fiber model can be very time-consuming, due to the complex nature of the calculations involved. Here, a surrogate (approximate) model of such an auditory nerve fiber model was developed using machine learning methods, to perform simulations more efficiently. Several machine learning models were compared, of which a Convolutional Neural Network showed the best performance. In fact, the Convolutional Neural Network was able to emulate the behavior of the auditory nerve fiber model with extremely high similarity ( R 2 > 0 . 99 ), tested under a wide range of experimental conditions, whilst reducing the simulation time by five orders of magnitude. In addition, a method for randomly generating charge-balanced waveforms using hyperplane projection is introduced. 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 of energy efficiency. The resulting waveforms resemble a positive Gaussian-like peak, preceded by an elongated negative phase. When comparing the energy of the waveforms generated by the Evolutionary Algorithm with the commonly used square wave, energy decreases of 8%-45% were observed for differ-ent pulse durations. These results were validated with the original auditory nerve fiber model, which demonstrates that the proposed surrogate model can be used as its accurate and efficient replacement.(c) 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ) Show less
Welcome to the Proceedings of the 12th Conference on Evolutionary Multi-Criterion Optimization (EMO), held in Leiden, The Netherlands, March 20–24, 2023 in hybrid format.Why hold EMO conferences?...Show moreWelcome to the Proceedings of the 12th Conference on Evolutionary Multi-Criterion Optimization (EMO), held in Leiden, The Netherlands, March 20–24, 2023 in hybrid format.Why hold EMO conferences? This question was discussed at EMO 2007 by its founders. The doubts regarding the viability of the conferences were fortunately cast away as the importance, need, and ubiquity of multi-criterion optimization keeps growing each year at a tremendous pace, impacting other areas and being influenced itself by them.For millennia optimization (improving things) has played a crucial role for humans. In more recent times, EMO (and optimization in general) has become important in sciencein areas such as physics, biology, economics, social sciences,medical sciences, and mathematics. For instance, Snell’s law was discovered by Willebrord Snellius through experimentation, only later it was realized that it can be derived from Fermat’s principle of least time, stating that light always chooses the path that is traveled in the least time.As such, the laws of nature can often be perceived as a process of optimization and optimal decision-making. Secondly, another use of optimization is the following. Many insights can be gained by looking at extremal objects (for instance, given 2n points in the plane no three of which lie on a line, n of them blue and n of them red, it is always possible to create n line segments by using the given points such that the endpoints have different colors and no two segments intersect – this can be understood by looking at the appropriate extremal object). Thirdly, methodologies and techniques developed in the EMO community have been empowering many practical scenarios: from finding the best taxation system, the best returns on investments while avoiding too high risks,discovering potent drug candidates with few side effects, to designing engineering structures that optimally balance the energy consumption and the environmental impact (e.g., minimizing the CO2 or CH4 emission).In the EMO conferences, we focus mainly on the evolutionary approaches to solving multi-criterion optimization and decision-making problems since the applicability of analytical/deterministic methods is often limited. For the scenarios where both categories of approaches are applicable, the hybridizations of analytical and evolutionary algorithms have appeared over the years, combining the strengths of both categories.Such hybridizations were also covered in the EMO conferences. In recent years, the EMOcommunity has been bridged with theMulti-Criterion Decision-Making (MCDM) community, which focuses more on the decision-making aspects of the same problem. According to the EMO tradition, also in this year’s event, many works are dedicated to designing and studying algorithms, ranging from novel algorithmic operators to thetheoretical analysis of existing ones. Notably, there are some contributions that connect EMO with Machine Learning/Artificial Intelligence, which draws more and more research interests nowadays. Also, appropriate attention – also as a tutorial – is paid tobenchmarking and empirical performance assessment, for instance, new benchmarking vi Preface problem sets. Furthermore, some submissions address real-world problems using EMO methodologies, which nicely complete the scope of the conference.Show less
The conflict between computational budget and quality of found solutions is crucial when dealing with expensive black-box optimization problems from the industry. We show that through multi... Show moreThe conflict between computational budget and quality of found solutions is crucial when dealing with expensive black-box optimization problems from the industry. We show that through multi-objective parameter tuning of the Covariance Matrix Adaptation Evolution Strategy on benchmark functions different optimal algorithm configurations can be found for specific computational budgets and solution qualities. With the obtained Pareto front, tuned parameter sets are selected and transferred to a real-world optimization problem from vehicle dynamics, improving the solution quality and budget needed. The benchmark functions for tuning are selected based on their similarity to a real-world problem in terms of Exploratory Landscape Analysis features. Show less
Tannemaat, M.R.; Kefalas, M.; Geraedts, V.J.; Remijn-Nelissen, L.; Verschuuren, A.J.M.; Koch, M.; ... ; Bäck, T.H.W. 2023
OBJECTIVE\nMETHODS\nRESULTS\nCONCLUSIONS\nSIGNIFICANCE\nDistinguishing normal, neuropathic and myopathic electromyography (EMG) traces can be challenging. We aimed to create an automated time... Show moreOBJECTIVE\nMETHODS\nRESULTS\nCONCLUSIONS\nSIGNIFICANCE\nDistinguishing normal, neuropathic and myopathic electromyography (EMG) traces can be challenging. We aimed to create an automated time series classification algorithm.\nEMGs of healthy controls (HC, n = 25), patients with amyotrophic lateral sclerosis (ALS, n = 20) and inclusion body myositis (IBM, n = 20), were retrospectively selected based on longitudinal clinical follow-up data (ALS and HC) or muscle biopsy (IBM). A machine learning pipeline was applied based on 5-second EMG fragments of each muscle. Diagnostic yield expressed as area under the curve (AUC) of a receiver-operator characteristics curve, accuracy, sensitivity, and specificity were determined per muscle (muscle-level) and per patient (patient-level).\nDiagnostic yield of the classification ALS vs. HC was: AUC 0.834 ± 0.014 at muscle-level and 0.856 ± 0.009 at patient-level. For the classification HC vs. IBM, AUC was 0.744 ± 0.043 at muscle-level and 0.735 ± 0.029 at patient-level. For the classification ALS vs. IBM, AUC was 0.569 ± 0.024 at muscle-level and 0.689 ± 0.035 at patient-level.\nAn automated time series classification algorithm can distinguish EMGs from healthy individuals from those of patients with ALS with a high diagnostic yield. Using longer EMG fragments with different levels of muscle activation may improve performance.\nIn the future, machine learning algorithms may help improve the diagnostic accuracy of EMG examinations. Show less
The algorithm selection problem is of paramount importance in achieving high-quality results while minimizing computational effort, especially when dealing with expensive black-box optimization... Show moreThe algorithm selection problem is of paramount importance in achieving high-quality results while minimizing computational effort, especially when dealing with expensive black-box optimization problems. In this paper, we address this challenge by using randomly generated artificial functions that mimic the landscape characteristics of the original problem while being inexpensive to evaluate. The similarity between the artificial function and the original problem is quantified using Exploratory Landscape Analysis. We demonstrate a significant performance improvement on five real-world vehicle dynamics problems by transferring the parameters of the Covariance Matrix Adaptation Evolution Strategy tuned to these artificial functions.We provide a complete set of simulated values of braking distance for fully enumerated 2D design spaces of all five real-world optimization problems. So, replication of our results and benchmarking directly on the real-world problems is possible. Beyond the scope of this paper, this data can be used as a benchmarking set for multi-objective optimization with up to five objectives. Show less
The performance of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is significantly affected by the selection of the specific CMA-ES variant and the parameter values used. Furthermore,... Show moreThe performance of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is significantly affected by the selection of the specific CMA-ES variant and the parameter values used. Furthermore, optimal CMA-ES parameter configurations vary across different problem landscapes, making the task of tuning CMA-ES to a specific optimization problem a challenging mixed-integer optimization problem. In recent years, several advanced algorithms have been developed to address this problem, including the Sequential Model-based Algorithm Configuration (SMAC) and the Tree-structured Parzen Estimator (TPE).In this study, we propose a novel approach for tuning CMA-ES by leveraging CMA-ES itself. Therefore, we combine the modular CMA-ES implementation with the margin extension to handle mixed-integer optimization problems. We show that CMA-ES can not only compete with SMAC and TPE but also outperform them in terms of wall clock time. Show less
Back, T.H.W.; Kononova, A.V.; Stein, N. van; Wang, H.; Antonov, K.; Kalkreuth, R.T.; ... ; Ye, F. 2023
Thirty years, 1993–2023, is a huge time frame in science. We address some major developments in the field of evolutionary algorithms, with applications in parameter optimization, over these 30... Show moreThirty years, 1993–2023, is a huge time frame in science. We address some major developments in the field of evolutionary algorithms, with applications in parameter optimization, over these 30 years. These include the covariance matrix adaptation evolution strategy and some fast-growing fields such as multimodal optimization, surrogate-assisted optimization, multiobjective optimization, and automated algorithm design. Moreover, we also discuss particle swarm optimization and differential evolution, which did not exist 30 years ago, either. One of the key arguments made in the paper is that we need fewer algorithms, not more, which, however, is the current trend through continuously claiming paradigms from nature that are suggested to be useful as new optimization algorithms. Moreover, we argue that we need proper benchmarking procedures to sort out whether a newly proposed algorithm is useful or not. We also briefly discuss automated algorithm design approaches, including configurable algorithm design frameworks, as the proposed next step toward designing optimization algorithms automatically, rather than by hand. Show less
Antonov, K.; Kononova, A.V.; Bäck, T.H.W.; Stein, N. van 2023
ObjectiveDistinguishing normal, neuropathic and myopathic electromyography (EMG) traces can be challenging. We aimed to create an automated time series classification algorithm.MethodsEMGs of... Show moreObjectiveDistinguishing normal, neuropathic and myopathic electromyography (EMG) traces can be challenging. We aimed to create an automated time series classification algorithm.MethodsEMGs of healthy controls (HC, n = 25), patients with amyotrophic lateral sclerosis (ALS, n = 20) and inclusion body myositis (IBM, n = 20), were retrospectively selected based on longitudinal clinical follow-up data (ALS and HC) or muscle biopsy (IBM). A machine learning pipeline was applied based on 5-second EMG fragments of each muscle. Diagnostic yield expressed as area under the curve (AUC) of a receiver-operator characteristics curve, accuracy, sensitivity, and specificity were determined per muscle (muscle-level) and per patient (patient-level).ResultsDiagnostic yield of the classification ALS vs. HC was: AUC 0.834 ± 0.014 at muscle-level and 0.856 ± 0.009 at patient-level. For the classification HC vs. IBM, AUC was 0.744 ± 0.043 at muscle-level and 0.735 ± 0.029 at patient-level. For the classification ALS vs. IBM, AUC was 0.569 ± 0.024 at muscle-level and 0.689 ± 0.035 at patient-level.ConclusionsAn automated time series classification algorithm can distinguish EMGs from healthy individuals from those of patients with ALS with a high diagnostic yield. Using longer EMG fragments with different levels of muscle activation may improve performance. Show less
Rooij, L. van; Winter, R. de; Kononova, A.V.; Stein, N. van 2022
Decisions made in the early phases of ship design have a large influence on the capital and operational expenses of a vessel. In order to support decision making in this phase, big data and machine... Show moreDecisions made in the early phases of ship design have a large influence on the capital and operational expenses of a vessel. In order to support decision making in this phase, big data and machine learning techniques can be of great use. This work shows how Explainable Artificial Intelligence (XAI) and Global Sensitivity Analysis (GSA) combined with Autonomous Identification System (AIS) and static ship data can be used to find important design characteristics of ships. A data collection framework is setup that collects AIS data over a five month time period. Static ship design data is used to predict performance related target features that are calculated from AIS data. By applying XAI and GSA methods to the regression models that predict these target features, insight can be gained on how design features influence the performance of ships. Experiments showed that for most ship types, the overall length is the most important design feature for speed related target features. Besides the overall length, the draught also has a significant impact on the rotation capabilities. Show less
Vermetten, D.L.; Caraffini, F.; Stein, B. van; Kononova, A.V. 2022
Automated Machine Learning (AutoML) frameworks are designed to select the optimal combination of operators and hyperparameters. Classical AutoML-based Bayesian Optimization (BO) approaches often... Show moreAutomated Machine Learning (AutoML) frameworks are designed to select the optimal combination of operators and hyperparameters. Classical AutoML-based Bayesian Optimization (BO) approaches often integrate all operator search spaces into a single search space. However, a disadvantage of this history-based strategy is that it can be less robust when initialized randomly than optimizing each operator algorithm combination independently. To overcome this issue, a novel contesting procedure algorithm, Divide And Conquer Optimization (DACOpt), is proposed to make AutoML more robust. DACOpt partitions the AutoML search space into a reasonable number of sub-spaces based on algorithm similarity and budget constraints. Furthermore, throughout the optimization process, DACOpt allocates resources to each sub-space to ensure that (1) all areas of the search space are covered and (2) more resources are assigned to the most promising sub-space. Two extensive sets of experiments on 117 benchmark datasets demonstrate that DACOpt achieves significantly better results in 36% of AutoML benchmark datasets: 5% when to compared to TPOT, 8% - to AutoSklearn, 15% - to H20 and 18% - to ATM. Show less
