# Lars Willmes's research while affiliated with Leiden University and other places

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## Publications (12)

In recent years, the distribution grid planning process has faced the big challenge to integrate renewable energy sources in its planning methodology while preserving a secure and stable provision of electricity. With the currently observable efforts to electrify human mobility all around the world, another new challenge arises for the planning and...

Background: Electronic patient files generate an enormous amount of medical data. These data can be used for research, such as prognostic modelling. Automatisation of statistical prognostication processes allows automatic updating of models when new data is gathered. The increase of power behind an automated prognostic model makes its predictive ca...

Background:
Electronic patient files generate an enormous amount of medical data. These data can be used for research, such as prognostic modeling. Automatization of statistical prognostication processes allows automatic updating of models when new data is gathered. The increase of power behind an automated prognostic model makes its predictive ca...

This study describes first steps taken to bring evolutionary optimization technology from computer simulations to real world
experimentation in physics laboratories. The approach taken considers a well understood Laser Pulse Compression problem accessible
both to simulation and laboratory experimentation as a test function for variants of Evolution...

Neural networks and Kriging method are compared for constructing fitness approximation models in evolutionary optimization algorithms. The two models are applied in an identical framework to the optimization of a number of well known test functions. In addition, two different ways of training the approximators are evaluated: in one setting the mode...

Evolving solutions rather than computing them certainly represents an unconventional programming approach. The general method- ology of evolutionary computation has already been known in computer science since more than 40 years, but their utilization to program other algorithms is a more recent invention. In this paper, we outline the ap- proach b...

Computer simulations of complex engineering problems have become a standard tool of modern product development and design. The increasing computational power at modest costs leads to a growing interest in directly using computer simulation codes for automatic product optimization. Traditional numerical optimization methods have some drawbacks that...

Computer simulations of complex engineering problems have become a standard tool of modern product development and design. The increasing computational power at modest costs leads to a growing interest in directly using computer simulation codes for automatic product optimization. Traditional numerical optimization methods have some drawbacks that...

In this paper we will describe the optimisation of a two-criteria wing-design problem where calculation of the objective function
requires the solution of the two-dimensional Navier-Stokes equations. It will be shown that basic concepts of the Strength
Pareto Evolutionary Algorithm 2 (SPEA2) and the Non dominated Sorting Genetic Algorithm II (NSGA...

A new approach for multi criteria design optimisation is presented in the paper. The problem tackled with this approach is
the 2- dimensional design of an aircraft wing. To carry the derandomized step size control also to the multi criteria applications,
four different selection schemes are proposed. Furthermore, we present a new method for averagi...

Evolving solutions rather than computing them certainly represents an unconventional programming approach. The general method- ology of evolutionary computation has already been known in computer science since more than 40 years, but their utilization to program other algorithms is a more recent invention. In this paper, we outline the ap- proach b...

## Citations

... How iteratively interacting process modules and information compression could be realised, was also part of the Agent.GridPlan project and can be reviewed in [12]. ...

... For this type of data, the proportional hazard model which is popularized by Cox is the suitable model. Even though the concept of the Cox PH model allows for modeling different levels of risk for different subgroups, it does not control risk levels for some relevant covariates that are often unavailable/unobserved to the researcher or even unknown [19][20][21]. Frailty models, on the other hand, which are the analog to the survival data of regression models, take into account heterogeneity and random effects. A shared frailty model is a random-effects model in which frailties are common among groups of individuals or spells and are distributed randomly across them [20][21][22][23]. ...

... Effective use of evolutionary and directional local search operators within available computational budget can be implemented https Subscripts avg averaged value during the optimization process by adaptive selection of p ls , probability for the local search operator [16]. Application of EAs to single-and multi-objective aerodynamic design optimization problems has been an active research topic in recent years [1,[17][18][19][20]. It is noted that past hybrid methods applied to aerodynamic optimization combining an EMOA and a gradientbased optimization method do not provide a general strategy on how to determine the frequency of conducting the gradient-based optimization [1]. ...

... Many efforts have been made in the direction of developing automated methods for constructing CAs based on observed space-time diagrams. These include methods based on genetic algorithms (Bolt et al., 2018;Richards et al., 1990;Mitchell et al., 1996;Bäck et al., 2005;Sapin et al., 2003), genetic programming (Bandini et al., 2008;Maeda and Sakama, 2007;Andre et al., 1996), gene expression programming (Ferreira, 2001), other evolutionary algorithms (Kroczek and Zelinka, 2018), ant colony algorithms (Liu et al., 2008), machine learning approaches (Bull and Adamatzky, 2007;Gilpin, 2018), as well as direct construction algorithms (Adamatzky, 1994;Billings, 2000,2000;Sun et al., 2011). A review of the key methods is presented in Adamatzky (2012). ...

... Solving this problem may require a simultaneous optimization of all profiles while considering stacking constraints. Optimization algorithms for direct design are mainly gradient-based [18,32,72,92] methods and stochastic algorithms [82,114,119]. Also hybrid approaches combining gradient-based methods and stochastic algorithms are employed [67]. ...

... In recent years the interest in the QC field grew also within the Computational Intelligence community, likely due to the popularity of Evolutionary Algorithms as the routinely employed optimization heuristics in closed QC learning loops (see, e.g., [23,24,25,26] ), in parallel to the growing understanding of QC search landscapes [27,28]. Furthermore, in the context of the current study, various DES routines also started to be deployed in the optimization of QC systems [29,30,31], along with the successful application of CMA-ES in experimental QC single-objective [32,33,34] and multi-objective [35] systems. The remainder of the manuscript is organized as follows. ...

... Since then many example problems have been published. For instance, Naujoks et al. presented a multi-objective shape optimisation problem for aerofoils, optimising high-lift and low-drag simultaneously using CFD simulations [2]. Similar problems with a particular attention to drag coefficients and uncertainty are available from [3,4]. ...

... The capability of the estimator to account for censored instances allows researchers to work with data in specific time frames in order to establish a feasible idea of survivability for certain diseases. Another statistical model that is commonly used in the world of survival analysis is the Cox proportional hazards regression [28], often abbreviated as the Cox regression. This model is one of the most common regression modeling frameworks that enables the exploration of prognostic factors and the estimation of survival rates [29]. ...

... Surrogate models are researched widely in the past twenty years [33]. Many machine learning methods can be used as surrogates, such as Kriging (also known as Gaussian processes) [8,23,32,43], radial basis function (RBF) [21,26,37], polynomial suppression models [48], [48], support vector machine (SVMs) [19,24], and some other neural networks [1,9,10,16,39]. Kriging is a widely studied surrogate model, which can estimate uncertainty information according to the mean square errors provided by the model. But the shortcoming of the Kriging is also obvious. ...