Purposeful model parameters genesis in simple genetic algorithms

Angelova, Maria; Atanassov, Krassimir; Pencheva, Tania

doi:10.1016/j.camwa.2012.01.047

Cited by 22 publications

(11 citation statements)

References 7 publications

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“…Scalar relative error tolerance RelTol is set to 1e -4 , while the vector of absolute error tolerances (all components) AbsTol -to 1e -5 . Parameter identification of the model (1)- (5) has been performed using Genetic Algorithm Toolbox [9] in Matlab 7 environment. All the computations are performed using a PC Intel Pentium 4 (2.4 GHz) platform running Windows XP.…”

Section: Resultsmentioning

confidence: 99%

“…When results from many algorithms executions were accumulated and analyzed, they show that the values of model parameters can be assembled and predefined boundaries could be restricted. That provoked the idea resulted in purposeful model parameters genesis (PMPG) [5] for shrinking variation boundaries of model parameters values, aiming to decrease convergence time while improve or at least save model accuracy.…”

Section: Procedures For Purposeful Model Parameters Genesismentioning

confidence: 99%

“…The procedure for PMPG has been originally developed for singlepopulation genetic algorithm [5] and consists of six steps, as shown in the right-side of the flowchart in Figure 1. Left side of the flowchart in Figure 1 presents the working principle of standard MpGA according to [1,6].…”

Section: Procedures For Purposeful Model Parameters Genesismentioning

confidence: 99%

“…The effectiveness of a certain optimization technique can be evaluated by the model accuracy achieved and the convergence time needed. Obtained promising results when purposeful model parameter genesis was originally developed and successfully applied for simple genetic algorithm [5], provokes the idea such a procedure to be tested for another kind of genetic algorithm. Thus, the aim of the study is to apply and validate a procedure for purposeful genesis for parameter identification of S. cerevisiae fed-batch cultivation when using multipopulation genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Purposeful Model Parameters Genesis in Multi-population Genetic Algorithm

Pencheva¹,

Angelova²

2014

Glob J Tech Opt

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IIn this paper recently proposed procedure for purposeful model parameters genesis, originally developed for simple genetic algorithm, has been validated for multi-population genetic algorithm when it is applied for the purposes of parameter identification of S. cerevisiae fed-batch cultivation. Proposed procedure aims to improve the algorithm effectiveness in respect to the convergence time and model accuracy treating intervals of variations of model parameters. Obtained results after the procedure application show more than 12% improvement of multi-population genetic algorithm convergence time while saving and even slightly meliorating the model accuracy.

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Section: Resultsmentioning

confidence: 99%

Section: Procedures For Purposeful Model Parameters Genesismentioning

confidence: 99%

Section: Procedures For Purposeful Model Parameters Genesismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Purposeful Model Parameters Genesis in Multi-population Genetic Algorithm

Pencheva¹,

Angelova²

2014

Glob J Tech Opt

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“…Several heuristic algorithms were proposed for this problem: the degree-constrained shortest path heuristic SPH [2] and the degree-constrained kruskal shortest path heuristic algorithm K.SPH [3]. The first, we study genetic algorithm [4][5][6] in-depth and grasp the basic idea of genetic algorithms and problem-solving steps. Finally, we design and realize degree-constrained multicast routing algorithm based on genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Degree-constrained minimum spanning tree problem using genetic algorithm

Liu

Chen

Abraham

et al. 2012

2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC)

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Abstract-Computer network technology has been growing explosively and the multicast technology has become a hot Internet research topic. The main goal of multicast routing algorithm is seeking a minimum cost multicast tree in a given network, also known as the Steiner tree problem, which is a classical NP-Complete problem. We measure the multicast capability of each node through the degree-constraint for each node and discuss the problem of multicast in the case of degree-constraint, which has an important significance in the communication network. Limiting the capacity of each node during the replication process of information transmission can improve the speed of the network, which has an important significance in real-time service. In this paper, we solve constrained multicast routing algorithm based on genetic algorithm. The idea is to simulate the Darwinian theory of biological evolution. At the same time, we improve the generating random tree and replace the variation by the combination of the two variations. On one hand, we improve the efficiency of generating random tree and on the other hand, we can control the mutation of different variations in a more flexible manner.

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