Population based optimal experimental design in cancer diagnosis and chemotherapy: In silico analysis

Kiran, Kanchi Lakshmi; Samavedham, Lakshminarayanan

doi:10.1016/j.jprocont.2012.12.009

“…GA is a metaheuristic optimization algorithm that is based on the process of natural selection. [46][47][48] A population of candidate solutions (also termed as individuals in GA) evolves towards the optimal solution via different biologically inspired operations such as mutation, crossover, and selection. 49,50 Each individual is represented by a vector of decision variables involved in the optimization problem (i.e., 'L' in this study).…”

Section: Optimization Frameworkmentioning

confidence: 99%

Rational design of antimicrobial peptides: an optimization approach

Sadasivam,

Nambiar,

Dutta

et al. 2024

Mol. Syst. Des. Eng.

6

0

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“…In the field of chemical engineering some works focusing on model-based experiment design (i.e., design of experiments for improvement of parameter precision and / or model discrimination) using Pareto optimization started to appear in recent years [15], [12], [16]- [24]. In the works of [19], [24] the trade-off between experimental effort and information gain is studied. Other works have focused on using MOO approaches to find trade-offs between maximizing the information content and minimizing parameter correlation [17], [18], [20].…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of Laboratory and Pilot-plant Experiments using Multiobjective Optimization

Forte¹,

Harbou²,

Burger³

et al. 2021

Preprint

0

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Performing an experimental design prior to the collection of data is in most circumstances important to ensure efficiency. The focus of this work is the combination of model‐based and statistical approaches to optimal design of experiments. The knowledge encoded in the model is used to identify the most interesting range for the experiments via a Pareto optimization of the most important conflicting objectives. Analysis of the trade‐offs found is in itself useful to design an experimental plan. This can be complemented using a factorial design in the most interesting part of the Pareto frontier.

show abstract

“…In the field of chemical engineering some works focusing on model-based experiment design (i.e., design of experiments for improvement of parameter precision and / or model discrimination) using Pareto optimization started to appear in recent years [15], [12], [16]- [24]. In the works of [19], [24] the trade-off between experimental effort and information gain is studied. Other works have focused on using MOO approaches to find trade-offs between maximizing the information content and minimizing parameter correlation [17], [18], [20].…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of Laboratory and Pilot-plant Experiments using Multiobjective Optimization

Forte¹,

Harbou²,

Burger³

et al. 2018

Preprint

View full text Add to dashboard Cite

Performing an experimental design prior to the collection of data is in most circumstances important to ensure efficiency. The focus of this work is the combination of model‐based and statistical approaches to optimal design of experiments. The knowledge encoded in the model is used to identify the most interesting range for the experiments via a Pareto optimization of the most important conflicting objectives. Analysis of the trade‐offs found is in itself useful to design an experimental plan. This can be complemented using a factorial design in the most interesting part of the Pareto frontier.

show abstract

Population based optimal experimental design in cancer diagnosis and chemotherapy: In silico analysis

Cited by 5 publications

References 28 publications

Rational design of antimicrobial peptides: an optimization approach

Rational design of antimicrobial peptides: an optimization approach

Optimal Design of Laboratory and Pilot-plant Experiments using Multiobjective Optimization

Optimal Design of Laboratory and Pilot-plant Experiments using Multiobjective Optimization

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