A genetic algorithms based multi-objective neural net applied to noisy blast furnace data

Pettersson, Frank; Chakraborti, Nirupam; Saxén, Henrik

doi:10.1016/j.asoc.2005.09.001

Cited by 235 publications

(186 citation statements)

References 16 publications

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“…In term of these two objectives, one can thus think of working out a Pareto tradeoff, where each solution in the Pareto frontier denotes a neural network of a unique architecture with a unique set of weights. A procedure for evolving such frontiers through a Predator-Prey type multi-objective Genetic Algorithm (Li, 2003) has been demonstrated in a recent article (Pettersson et al, 2007a) and was elaborately tested on the highly nonlinear data from an industrial iron making blast furnace shown schematically in Figure 11.3. The aim of the study reported in Pettersson et al (2007a) was to evolve a neural network that would optimally predict the carbon, sulfur and silicon contents of the hot metal produced in the blast furnace as a function of a number of process parameters.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…A procedure for evolving such frontiers through a Predator-Prey type multi-objective Genetic Algorithm (Li, 2003) has been demonstrated in a recent article (Pettersson et al, 2007a) and was elaborately tested on the highly nonlinear data from an industrial iron making blast furnace shown schematically in Figure 11.3. The aim of the study reported in Pettersson et al (2007a) was to evolve a neural network that would optimally predict the carbon, sulfur and silicon contents of the hot metal produced in the blast furnace as a function of a number of process parameters. What was attempted there was simultaneously to minimize (i) the training error of the network (E) and (ii) the required number of active connections in the lower part of it (N ).…”

Section: Problem Descriptionmentioning

confidence: 99%

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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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Section: Problem Descriptionmentioning

confidence: 99%

Section: Problem Descriptionmentioning

confidence: 99%

Real-World Applications of Multiobjective Optimization

Stewart

Bandte

Braun

et al. 2008

Multiobjective Optimization

View full text Add to dashboard Cite

show abstract

“…The optimization trajectory of the 2D space of the objectives c trn and c reg was controlled by modifying BP weight update rules using two sliding surface control indicators each belongs to the mentioned objectives, respectively Multiobjective treatment to FNN was also offered by using improvising metaheuristics itself such as a predator-prey algorithm was proposed in [292]. To get a generalized network, the predator-prey algorithm used a family of the randomly generated population of sparse neural networks, called pray population and an externally induced family of predators population whose job was to prune preys populations based on the objectives c trn and c net was also generated.…”

Section: Non-pareto Based Multiobjective Approachesmentioning

confidence: 99%

Metaheuristic design of feedforward neural networks: A review of two decades of research

Ojha

Abraham

Snel

2017

Engineering Applications of Artificial Intelligence

441

154

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Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era.

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“…Pettersson et al [63] used an evolutionary multiobjective technique in the training process of a feed forward neural network, using noisy data from an industrial iron blast furnace. The number of nodes in the hidden layer, the architecture of the lower part of the network, as well as the weights used in them were kept as variables, and a Pareto front was effectively constructed by minimizing the training error along with the network size.…”

Section: Multiobjective Evolutionary Design Of Intelligent Paradigmsmentioning

confidence: 99%

Engineering Evolutionary Intelligent Systems: Methodologies, Architectures and Reviews

Abraham

Groşan

2008

Engineering Evolutionary Intelligent Systems

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Summary. Designing intelligent paradigms using evolutionary algorithms is getting popular due to their capabilities in handling several real world problems involving complexity, noisy environment, imprecision, uncertainty and vagueness. In this Chapter, we illustrate the various possibilities for designing intelligent systems using evolutionary algorithms and also present some of the generic evolutionary design architectures that has evolved during the last couple of decades. We also provide a review of some of the recent interesting evolutionary intelligent system frameworks reported in the literature.

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A genetic algorithms based multi-objective neural net applied to noisy blast furnace data

Cited by 235 publications

References 16 publications

Real-World Applications of Multiobjective Optimization

Real-World Applications of Multiobjective Optimization

Metaheuristic design of feedforward neural networks: A review of two decades of research

Engineering Evolutionary Intelligent Systems: Methodologies, Architectures and Reviews

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