Controlled Elitist Non-dominated Sorting Genetic Algorithms for Better Convergence

Deb, Kalyanmoy; Goel, Tushar

doi:10.1007/3-540-44719-9_5

Cited by 336 publications

(241 citation statements)

References 6 publications

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“…There are some different Pareto-based multiobjective strategies that could also be applied for the same problem, as can be seen in [6]. Later in this paper, we will evaluate a more recent approach, the NSGA-II algorithm [9,10].…”

Section: Evolutionary Transparent Modeling Of Chaotic Systemsmentioning

confidence: 99%

“…Our selection operator is a variation of that used in the NSGA-II algorithm [9,10]. In the first place, the set of nondominated search points is computed by pairwise comparisons of all individuals in X .…”

Section: The Selection Operatormentioning

confidence: 99%

“…The NSGA-II is an implementation of the Pareto-based multiobjective genetic algorithm detailed in [9,10], which is currently assumed to be among the best available implementations of such kind of algorithms. The results will be shown with two different methodologies, graphical and statistical.…”

Section: Benchmark Problemsmentioning

confidence: 99%

“…Since the evaluation of the Lyapunov exponents is very time costly, we also propose to use our own custom evolutionary algorithm, that combine a tree-based codification with a populationbased, multiobjective extension of the Simulated Annealing. The algorithm that we propose in this paper is able to find a set of difference equations that reproduces the dynamics of a given chaotic time series, and improves the results of modern multiobjective evolutionary algorithms like NSGA-2 [9,10] when the number of evaluations of the objective function is limited.…”

Section: Introductionmentioning

confidence: 95%

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Multiobjective Evolutionary Search of Difference Equations-based Models for Understanding Chaotic Systems

Sánchez

Villar

2008

Foundations of Generic Optimization

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In control engineering, it is well known that many physical processes exhibit a chaotic component. In point of fact, it is also assumed that conventional modeling procedures disregard it, as stochastic noise, beside nonlinear universal approximators (like neural networks, fuzzy rule-based or genetic programming-based models,) can capture the chaotic nature of the process.In this chapter we will show that this is not always true. Despite the nonlinear capabilities of the universal approximators, these methods optimize the one step prediction of the model. This is not the most adequate objective function for a chaotic model, because there may exist many different nonchaotic processes that have near zero prediction error for such an horizon. The learning process will surely converge to one of them. Unless we include in the objective function some terms that depend on the properties on the reconstructed attractor, we may end up with a non chaotic model. Therefore, we propose to follow a multiobjective approach to model chaotic processes, and we also detail how to apply either genetic algorithms or simulated annealing to obtain a difference equations-based model.

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Section: Evolutionary Transparent Modeling Of Chaotic Systemsmentioning

confidence: 99%

Section: The Selection Operatormentioning

confidence: 99%

Section: Benchmark Problemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

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Multiobjective Evolutionary Search of Difference Equations-based Models for Understanding Chaotic Systems

Sánchez

Villar

2008

Foundations of Generic Optimization

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“…We used the multi-objective evolutionary algorithm called NSGA-II [2]. It is based on the constrained domination concept.…”

Section: The Multi-objective Evolutionary Approachmentioning

confidence: 99%

Multi-objective Evolutionary Probe Design Based on Thermodynamic Criteria for HPV Detection

Lee

Kim

Zhang

2004

PRICAI 2004: Trends in Artificial Intelligence

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Abstract. DNA microarrays are widely used techniques in molecular biology and DNA computing area. It consists of the DNA sequences called probes, which are DNA complementaries to the genes of interest, on solid surfaces. And its reliability seriously depends on the quality of the probe sequences. Therefore, one must carefully choose the probe sets in target sequences. In this paper, the probe design for DNA microarrays is formulated as the multi-objective optimization problem. We propose a multi-objective evolutionary approach, which is known to be suitable for this kind of optimization problem. Since a multi-objective evolutionary algorithm can find multiple solutions at a time, we used thermodynamic criteria to choose the most suitable one. For the experiments, the probe set generated by the proposed method is compared to the sequences used in commercial microarrays, which detects a set of Human Papillomavirus (HPV). The comparison result supports that our approach can be useful to optimize probe sequences.

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Inverse Design of Diffractive Relativistic Meta‐Sails via Multi‐Objective Optimization

Salary

Mosallaei

2021

Advcd Theory and Sims

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Photonic propulsion of light sails using the radiation pressure of an intense laser beam is a promising route to achieve relativistic velocities for deep space exploration. A successful photonic design of such relativistic sails requires efficient acceleration and maintaining stability of the beam‐riding across a Doppler‐broadened propulsion band due to considerable red‐shift of the wavelength in the frame of the moving sail. While efficient acceleration of the sail requires maximizing the optical force along the beam propagation direction, the stability degree of the sail depends on the magnitude of lateral forces in the transverse plane of the beam. This establishes a trade‐off between these two requirements which calls for their synergistic engineering. In this work, an evolutionary multi‐objective optimization technique for the inverse design of diffractive relativistic meta‐sails with a genetic pixelated geometry is adopted. The goal is to identify a set of Pareto optimal solutions which provides the designer with an intuitive understanding of the ultimate trade‐off between acceleration and stability degree of the sail in a vast design space, and allows for a well‐informed decision on the best design given the requirements for the acceleration time and distance as well as the tolerance with respect to mispointings and misalignments.

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Controlled Elitist Non-dominated Sorting Genetic Algorithms for Better Convergence

Cited by 336 publications

References 6 publications

Multiobjective Evolutionary Search of Difference Equations-based Models for Understanding Chaotic Systems

Multiobjective Evolutionary Search of Difference Equations-based Models for Understanding Chaotic Systems

Multi-objective Evolutionary Probe Design Based on Thermodynamic Criteria for HPV Detection

Inverse Design of Diffractive Relativistic Meta‐Sails via Multi‐Objective Optimization

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