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Tetsuo Morimoto

Bio: Tetsuo Morimoto is an academic researcher from Ehime University. The author has contributed to research in topics: Intelligent control & Artificial neural network. The author has an hindex of 16, co-authored 82 publications receiving 898 citations.


Papers
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TL;DR: In this article, an optimal pattern of the heat treatment for tomatoes was investigated based on their surface color, using an intelligent control technique consisting of neural networks and genetic algorithms, and two types of optimal heat treatments were obtained.

83 citations

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TL;DR: In this article, the ARMA model was identified using the least squares method, and an intelligent control technique using genetic algorithms was applied to the control of the physiological processes of plants.

77 citations

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TL;DR: In this article, a new technique was proposed to evaluate the fruit shape quantitatively using attractor, fractal dimension and neural networks, and significant correlations were observed in their relationships.

65 citations

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TL;DR: In this article, a new intelligent control technique, including neural networks and genetic algorithms, for realizing the optimal control of the fruit-storage process was presented, where the control input is a relative humidity h, and the control outputs are two types of fruit responses: the water loss W h ( t ) and the development of lesion by fungi D h( t ).

56 citations

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TL;DR: In this paper, a hierarchical intelligent control system consisting of an expert system and an optimizer based on neural networks and genetic algorithms was used to optimize a total plant production process, where environmental factors in the cultivation and storage processes were optimally controlled.

51 citations


Cited by
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Journal ArticleDOI
Xin Yao1
01 Sep 1999
TL;DR: It is shown, through a considerably large literature review, that combinations between ANNs and EAs can lead to significantly better intelligent systems than relying on ANNs or EAs alone.
Abstract: Learning and evolution are two fundamental forms of adaptation. There has been a great interest in combining learning and evolution with artificial neural networks (ANNs) in recent years. This paper: 1) reviews different combinations between ANNs and evolutionary algorithms (EAs), including using EAs to evolve ANN connection weights, architectures, learning rules, and input features; 2) discusses different search operators which have been used in various EAs; and 3) points out possible future research directions. It is shown, through a considerably large literature review, that combinations between ANNs and EAs can lead to significantly better intelligent systems than relying on ANNs or EAs alone.

2,877 citations

Journal ArticleDOI
TL;DR: This paper attempts to provide a comprehensive overview of the related work within a unified framework on addressing different uncertainties in evolutionary computation, which has been scattered in a variety of research areas.
Abstract: Evolutionary algorithms often have to solve optimization problems in the presence of a wide range of uncertainties. Generally, uncertainties in evolutionary computation can be divided into the following four categories. First, the fitness function is noisy. Second, the design variables and/or the environmental parameters may change after optimization, and the quality of the obtained optimal solution should be robust against environmental changes or deviations from the optimal point. Third, the fitness function is approximated, which means that the fitness function suffers from approximation errors. Fourth, the optimum of the problem to be solved changes over time and, thus, the optimizer should be able to track the optimum continuously. In all these cases, additional measures must be taken so that evolutionary algorithms are still able to work satisfactorily. This paper attempts to provide a comprehensive overview of the related work within a unified framework, which has been scattered in a variety of research areas. Existing approaches to addressing different uncertainties are presented and discussed, and the relationship between the different categories of uncertainties are investigated. Finally, topics for future research are suggested.

1,528 citations

Journal ArticleDOI
Yaochu Jin1
01 Jan 2005
TL;DR: A comprehensive survey of the research on fitness approximation in evolutionary computation is presented, main issues like approximation levels, approximate model management schemes, model construction techniques are reviewed and open questions and interesting issues in the field are discussed.
Abstract: Evolutionary algorithms (EAs) have received increasing interests both in the academy and industry. One main difficulty in applying EAs to real-world applications is that EAs usually need a large number of fitness evaluations before a satisfying result can be obtained. However, fitness evaluations are not always straightforward in many real-world applications. Either an explicit fitness function does not exist, or the evaluation of the fitness is computationally very expensive. In both cases, it is necessary to estimate the fitness function by constructing an approximate model. In this paper, a comprehensive survey of the research on fitness approximation in evolutionary computation is presented. Main issues like approximation levels, approximate model management schemes, model construction techniques are reviewed. To conclude, open questions and interesting issues in the field are discussed.

1,228 citations

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TL;DR: In this article, the significant elements of a computer vision system and emphasises the important aspects of the image processing technique coupled with a review of the most recent developments throughout the food industry.

949 citations

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TL;DR: In this paper, the authors used a simulation-based Artificial Neural Network (ANN) to characterize building behavior, and then combined this ANN with a multiobjective Genetic Algorithm (NSGA-II) for optimization.

588 citations