Multicriterial optimization

Gerasimov, E. N.; Repko, V. N.

doi:10.1007/bf00883255

Cited by 21 publications

(19 citation statements)

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“…This fitness method is obtained by multiplying all the error items for each target time series and the fitness calculation formula is: Bridgman [8] was the first author to refer to this approach and later Gerasimov and Repko [17] successfully applied this method to the multi-objective optimization of a truss. These fitness functions were plugged into a nested Evolutionary Algorithm to evolve P system models that could match a biological phenotype that is specified through a collection of time series representing molecular concentrations of various species.…”

Section: Methodsmentioning

confidence: 99%

Evolutionary symbolic discovery for bioinformatics, systems and synthetic biology

Widera

Bacardit

Krasnogor

et al. 2010

Proceedings of the 12th Annual Conference Companion on Genetic and Evolutionary Computation

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Symbolic regression and modeling are tightly linked in many Bioinformatics, Systems and Synthetic Biology problems. In this paper we briefly overview two problems, and the approaches we have use to tackle them, that can be deemed to represent this entwining of regression and modeling, namely, the evolutionary discovery of (1) effective energy functions for protein structure prediction and (2) models that capture biological behavior at the gene, signaling and metabolic networks level. These problems are not, strictly speaking, "regression problems" but they do share several characteristics with the latter, namely, a symbolic representation of a solution is sought , this symbolic representation must be human understandable and the results obtained by the symbolic and human interpretable solution must fit the available data without over-learning.

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Section: Methodsmentioning

confidence: 99%

Evolutionary symbolic discovery for bioinformatics, systems and synthetic biology

Widera

Bacardit

Krasnogor

et al. 2010

Proceedings of the 12th Annual Conference Companion on Genetic and Evolutionary Computation

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“…Other researchers like Gerasimor and Repko (1978) have successfully apply the method to minimize the weight, displacement, and difficulty of construction, where the cross-sectional areas of the rods are the design variables, and constraints are strength and stability. However, the approach has not been used extensively, and the characteristics of the weights are unclear.…”

Section: Weighted Product Methods (Wpm)mentioning

confidence: 99%

Review of Multi-criteria Optimization Methods – Theory and Applications

Odu¹,

Charles-Owaba²

2013

IOSRJEN

108

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-A review of multi-criteria optimization concepts and methods is presented. The techniques provide solutions to the problems involving conflicting and multiple objectives. Several methods based on weighted averages, priority setting, outranking and their combinations are employed. The application and procedure in respect to material selection is presented. It is observed from the literature that no single approach is superior, rather, the selection of a specific method depends on the type of information that is provided in the problem, the designer"s preferences, the solution requirements and the availability of software adopted.

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“…We repeat this procedure K times and compute the average as the final fitness. Thus, the fitness calculation formula for this method is as follows:where is the random weight for the j th target time series generated at the n th time. Equally weighted product method (F4) : This fitness method is obtained by multiplying all the error items for each target time series and the fitness calculation formula is:Bridgman 1922 was the first author to refer to this approach and later Gerasimov and Repko 1978 successfully applied this method to the multi-objective optimization of a truss. A related idea was pursued by Straffin 1993.…”

Section: Methodsmentioning

confidence: 99%

Evolving cell models for systems and synthetic biology

et al. 2010

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This paper proposes a new methodology for the automated design of cell models for systems and synthetic biology. Our modelling framework is based on P systems, a discrete, stochastic and modular formal modelling language. The automated design of biological models comprising the optimization of the model structure and its stochastic kinetic constants is performed using an evolutionary algorithm. The evolutionary algorithm evolves model structures by combining different modules taken from a predefined module library and then it fine-tunes the associated stochastic kinetic constants. We investigate four alternative objective functions for the fitness calculation within the evolutionary algorithm: (1) equally weighted sum method, (2) normalization method, (3) randomly weighted sum method, and (4) equally weighted product method. The effectiveness of the methodology is tested on four case studies of increasing complexity including negative and positive autoregulation as well as two gene networks implementing a pulse generator and a bandwidth detector. We provide a systematic analysis of the evolutionary algorithm’s results as well as of the resulting evolved cell models.

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Multicriterial optimization

Cited by 21 publications

References 0 publications

Evolutionary symbolic discovery for bioinformatics, systems and synthetic biology

Evolutionary symbolic discovery for bioinformatics, systems and synthetic biology

Review of Multi-criteria Optimization Methods – Theory and Applications

Evolving cell models for systems and synthetic biology

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