Logic Rules for Automated Synthesis of Function Models Using Evolutionary Algorithms

2021 ◽  
Author(s):  
Amaninder Singh Gil ◽  
Chiradeep Sen

Abstract This paper presents the development of logic rules for evaluating the fitness of function models synthesized by an evolutionary algorithm. A set of 65 rules for twelve different function verbs are developed. The rules are abstractions of the definitions of the verbs in their original vocabularies and are stated as constraints on the quantity, type, and topology of flows connected to the functions. The rules serve as an objective and unambiguous basis of evaluating the fitness of function models developed by a genetic algorithm. The said algorithm and the rules are implemented in software code, which is used to both demonstrate and validate the efficacy of the rule-based approach of converging function model synthesis using GAs.

Author(s):  
Amaninder Singh Gill ◽  
Chiradeep Sen

Abstract The goal of this paper is to develop the groundwork for automated synthesis of function models. To this end, an evolutionary algorithm based framework has been developed. A parameterization method that can completely describe any given function models has been proposed. The parameterization makes the function models compatible for use within the evolutionary algorithm framework. Validation of the parameterization method is carried out by using an evolutionary algorithm to synthesize the function models for five different electromechanical products. The algorithm converged in each case, indicating that the method is satisfactory and that function models can actually be synthesized using an evolutionary framework. In addition, the adaptation of several a priori rules for use in this framework has been proposed. These rules are categorized as grammar, logical and feature based rules. An updated evolutionary framework that incorporates these rules is also presented.


2008 ◽  
Vol 63 (2) ◽  
pp. 202-212 ◽  
Author(s):  
Ming-Hseng Tseng ◽  
Sheng-Jhe Chen ◽  
Gwo-Haur Hwang ◽  
Ming-Yu Shen

2021 ◽  
pp. 1-8
Author(s):  
Vania Karami ◽  
Giulio Nittari ◽  
Enea Traini ◽  
Francesco Amenta

Background: It is desirable to achieve acceptable accuracy for computer aided diagnosis system (CADS) to disclose the dementia-related consequences on the brain. Therefore, assessing and measuring these impacts is fundamental in the diagnosis of dementia. Objective: This study introduces a new CADS for deep learning of magnetic resonance image (MRI) data to identify changes in the brain during Alzheimer’s disease (AD) dementia. Methods: The proposed algorithm employed a decision tree with genetic algorithm rule-based optimization to classify input data which were extracted from MRI. This pipeline is applied to the healthy and AD subjects of the Open Access Series of Imaging Studies (OASIS). Results: Final evaluation of the CADS and its comparison with other systems supported the potential of the proposed model as a novel tool for investigating the progression of AD and its great ability as an innovative computerized help to facilitate the decision-making procedure for the diagnosis of AD. Conclusion: The one-second time response, together with the identified high accurate performance, suggests that this system could be useful in future cognitive and computational neuroscience studies.


Author(s):  
Khafiizh Hastuti ◽  
Azhari Azhari ◽  
Aina Musdholifah ◽  
Rahayu Supanggah

Author(s):  
Padmavathi .S ◽  
M. Chidambaram

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.


2007 ◽  
Vol 1 (4) ◽  
pp. 85-91
Author(s):  
Jeya S ◽  
◽  
Ramar K ◽  

2019 ◽  
Vol 50 (2) ◽  
pp. 98-112 ◽  
Author(s):  
KALYAN KUMAR JENA ◽  
SASMITA MISHRA ◽  
SAROJANANDA MISHRA ◽  
SOURAV KUMAR BHOI ◽  
SOUMYA RANJAN NAYAK

2010 ◽  
Vol 12 (1) ◽  
pp. 9-16 ◽  
Author(s):  
Xueying ZHNAG ◽  
Guonian LV ◽  
Boqiu LI ◽  
Wenjun CHEN

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