Genetic algorithms with a robust solution searching scheme

Tsutsui, Shigeyoshi; Ghosh, Ashish

doi:10.1109/4235.661550

Cited by 238 publications

(136 citation statements)

References 9 publications

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“…The EA ranks the solutions based on this expected value and subsequently performs selection, crossover and mutation. The self-averaging nature of EAs (Tsutsui and Ghosh, 1997) helps in the convergence of solutions, i.e. the solution vectors which have good fitness values survive through the generations and remain in subsequent populations.…”

Section: System Optimisation With Multi-objective Evolutionary Algorimentioning

confidence: 99%

A systems based approach for financial risk modelling and optimisation of the mineral processing and metal production industry

Pan

Korre

Durucan

2016

Computers & Chemical Engineering

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Large scale engineering process systems are subject to a variety of risks which affect the productivity and profitability of the industry in the long run. This paper outlines the short comings of the current methods of risk quantification and proposes a systems engineering framework to overcome these issues. The functionality of the developed model is illustrated for the case of mineral processing and metal production industries using a copper ore processing and refined metal production case study. The methodology provides a quantitative assessment of the risk factors and allows the opportunity to minimise financial losses, which would help investors, insurers and plant operators in these sectors to make appropriate risk hedging policies. The models developed can also be coupled with evolutionary or swarm based algorithms for optimising the systems. A numerical example is illustrated to demonstrate the validity of the proposition.

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Section: System Optimisation With Multi-objective Evolutionary Algorimentioning

confidence: 99%

A systems based approach for financial risk modelling and optimisation of the mineral processing and metal production industry

Pan

Korre

Durucan

2016

Computers & Chemical Engineering

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“…Robust solutions can be achieved in evolutionary optimization by a number of means. One simple approach is to add perturbations to the design variables or environmental parameters before the fitness is evaluated, which is known as implicit averaging, see e.g., Tsutsui and Ghosh (1997). An alternative to implicit averaging is explicit averaging, which means that the fitness value of a given design is averaged over a number of designs generated by adding random perturbations to the original design.…”

Section: Robustness Considerationsmentioning

confidence: 99%

Real-World Applications of Multiobjective Optimization

Stewart

Bandte

Braun

et al. 2008

Multiobjective Optimization

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Abstract. This chapter presents a number of illustrative case studies of a wide range of applications of multiobjective optimization methods, in areas ranging from engineering design to medical treatments. The methods used include both conventional mathematical programming and evolutionary optimization, and in one case an integration of the two approaches. Although not a comprehensive review, the case studies provide evidence of the extent of the potential for using classical and modern multiobjective optimization in practice, and opens many opportunities for further research. IntroductionThe intention with this chapter is to provide illustrations of real applications of multiobjective optimization, covering both conventional mathematical programming approaches and evolutionary multiobjective optimization. These illustrations do cover a broad range of application, but do not attempt to provide a comprehensive review of applications.

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“…Tsutsui et al [24] proposed a GA-based robust solution-searching scheme (RS 3 ) to evolve robust solutions. This approach works by adding perturbation noise to the design variables before fitness evaluation.…”

Section: Related Workmentioning

confidence: 99%

“…Application of evolutionary algorithms to traditional parametric robust design has been attracting increasing attention in the past decade [24][25][26]. Tsutsui et al [24] proposed a GA-based robust solution-searching scheme (RS 3 ) to evolve robust solutions.…”

Section: Related Workmentioning

confidence: 99%

A Novel Evolutionary Algorithm for Designing Robust Analog Filters

Zou

2018

Algorithms

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Designing robust circuits that withstand environmental perturbation and device degradation is critical for many applications. Traditional robust circuit design is mainly done by tuning parameters to improve system robustness. However, the topological structure of a system may set a limit on the robustness achievable through parameter tuning. This paper proposes a new evolutionary algorithm for robust design that exploits the open-ended topological search capability of genetic programming (GP) coupled with bond graph modeling. We applied our GP-based robust design (GPRD) algorithm to evolve robust lowpass and highpass analog filters. Compared with a traditional robust design approach based on a state-of-the-art real-parameter genetic algorithm (GA), our GPRD algorithm with a fitness criterion rewarding robustness, with respect to parameter perturbations, can evolve more robust filters than what was achieved through parameter tuning alone. We also find that inappropriate GA tuning may mislead the search process and that multiple-simulation and perturbed fitness evaluation methods for evolving robustness have complementary behaviors with no absolute advantage of one over the other.

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Genetic algorithms with a robust solution searching scheme

Cited by 238 publications

References 9 publications

A systems based approach for financial risk modelling and optimisation of the mineral processing and metal production industry

A systems based approach for financial risk modelling and optimisation of the mineral processing and metal production industry

Real-World Applications of Multiobjective Optimization

A Novel Evolutionary Algorithm for Designing Robust Analog Filters

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