11 papers · ranked by Valyu relevance
Moshe Sipper, Ryan J. Urbanowicz, Jason H. Moore
Finding the objective (i.e., goal or global optimum) in machine learning (ML) and related domains, such as evolutionary algorithms (EAs), invariably involves the definition of an objective function, which is the function we want to minimize or maximize . Any objective function implicitly defines an optimization…
Rachel Gaffney, Magnus Rattray, Jean-Marc Schwartz, Kate Meeson
There are huge variations in metabolic complexity between the different kingdoms of life. Whilst it has been shown that some simple, unicellular organisms such as E. coli direct their energetic resources towards maximising proliferation, the metabolic goals of more complex organisms are unclear. This is an especially…
Charalampos P. Triantafyllidis, Lazaros G. Papageorgiou, Marian Gheorghe
'Marian Gheorghe'] This paper presents a novel prototype platform that uses the same LaTeX mark-up language, commonly used to typeset mathematical content, as an input language for modeling optimization problems of various classes. The platform converts the LaTeX model into a formal Algebraic Modeling Language (AML)…
James Morrissey, Mariana Monteiro, Michael Betenbaugh, Cleo Kontoravdi
Metabolism reflects evolutionary priorities that govern how cells allocate resources. In mammalian cells, metabolic objectives are layered and context-dependent, making it difficult to pinpoint the priorities that underlie observed phenotypes. Here, we introduce ObjFind-M, an inverse optimization framework that infers…
Wolfgang Rannetbauer, Simon Hubmer, Carina Hambrock, Ronny Ramlau
Achieving both high quality and cost-efficiency are two critical yet often conflicting objectives in manufacturing and maintenance processes. Quality standards vary depending on the specific application, while cost-effectiveness remains a constant priority. These competing objectives lead to multi-objective…
Jiuyuan Huo, Liqun Liu
Parameter optimization of a hydrological model is intrinsically a high dimensional, nonlinear, multivariable, combinatorial optimization problem which involves a set of different objectives. Currently, the assessment of optimization results for the hydrological model is usually made through calculations and comparisons…
Barbara Schnitzer, Linnea Österberg, Marija Cvijovic
Flux balance analysis (FBA) is a powerful tool to study genome-scale models of the cellular metabolism, based on finding the optimal flux distributions over the network. While the objective function is crucial for the outcome, its choice, even though motivated by evolutionary arguments, has not been directly connected…
Wolfram Liebermeister
Cells need to make an efficient use of metabolites, proteins, energy, membrane space, and time, and resource allocation is also an important aspect of metabolism. How, for example, should cells distribute their protein budget between different cellular functions, e.g. different metabolic pathways, to maximise growth?…
Ozden Ustun
In multiobjective optimization methods, multiple conflicting objectives are typically converted into a single objective optimization problem with the help of scalarizing functions. The conic scalarizing function is a general characterization of Benson proper efficient solutions of non-convex multiobjective problems in…
Florian Häse, Loïc M. Roch, Alán Aspuru-Guzik
Chimera enables multi-target optimization for experimentation or expensive computations, where evaluations are the limiting factor.
Fabian Fröhlich, Peter K. Sorger
Ordinary differential equation (ODE) models are widely used to describe biochemical processes, since they effectively represent mass action kinetics. Optimization-based calibration of ODE models on experimental data can be challenging, even for low-dimensional problems. However, reliable model calibration is a…