Evolving Case-Based Reasoning with Genetic Algorithm in Wholesaler’s Returning Book Forecasting

2005 ◽  
pp. 205-214
Author(s):  
Pei-Chann Chang ◽  
Yen-Wen Wang ◽  
Ching-Jung Ting ◽  
Chien-Yuan Lai ◽  
Chen-Hao Liu
Keyword(s):  
Genetic Algorithm ◽  
Case Based Reasoning ◽  
Case Based

scholarly journals Automatic Case-Based Reasoning Approach for Landslide Detection: Integration of Object-Oriented Image Analysis and a Genetic Algorithm

2015 ◽  
Vol 7 (4) ◽  
pp. 4318-4342 ◽  
Author(s):  
Jie Dou ◽  
Kuan-Tsung Chang ◽  
Shuisen Chen ◽  
Ali Yunus ◽  
Jin-King Liu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Image Analysis ◽  
Object Oriented ◽  
Case Based Reasoning ◽  
Case Based

Case-based reasoning assisted explanation of genetic algorithm results

1993 ◽  
Vol 5 (1) ◽  
pp. 21-37 ◽  
Author(s):  
SUSHIL LOUIS ◽  
GARY McGRAW ◽  
RICHARD O. WYCKOFF
Keyword(s):  
Genetic Algorithm ◽  
Case Based Reasoning ◽  
Case Based

scholarly journals A hybrid reasoning system for supporting estuary modelling

2005 ◽  
Vol 7 (3) ◽  
pp. 185-198
Author(s):  
Sara Passone ◽  
Vahid Nassehi ◽  
Paul W. H. Chung
Keyword(s):  
Genetic Algorithm ◽  
Physical Phenomenon ◽  
Computing Methods ◽  
Case Based Reasoning ◽  
Illustrative Case ◽  
Detailed Knowledge ◽  
Reasoning System ◽  
Hybrid Reasoning ◽  
Illustrative Case Study ◽  
Case Based

In this paper the development of a Case-Based reasoning system for Estuarine Modelling (CBEM) is presented. The aim of the constructed CBEM system is to facilitate the utilisation of complex modelling software by users who lack detailed knowledge about modelling techniques and require training and assistance to implement sophisticated software effectively. The system is based on modern computing methods and is constructed as a hybrid of three modules which operate conjunctively to guide the user to obtain the best possible simulation for realistic problems. These modules are: a case-based reasoning scheme, a genetic algorithm and a library of numerical estuarine models. Based on the features of a given estuary and the physical phenomenon to be modelled, an appropriate solution algorithm from the system's library is retrieved by the case-based module after a specifically designed reasoning process. The selected model is then analysed and further treated by the genetic algorithm component to find the optimum parameters which can appropriately model the conditions and characteristics of any given estuary. Using these modules the steps that yield the best solution for a problem from the available hydrographic data under a set of specified conditions are explained. This is further elucidated by an illustrative case study which shows the applicability of the present CBEM system under realistic conditions. This case deals with the simulation of salinity distribution in the Tay estuary (Scotland, UK).

Optimization of a Gas Turbine Engine Rotor Disc Using Case-Based Reasoning and the GATE Genetic Algorithm

2010 ◽  
Author(s):  
S. Dominique ◽  
J.-Y. Tre´panier
Keyword(s):  
Genetic Algorithm ◽  
Gas Turbine ◽  
Structural Design ◽  
Gas Turbine Engine ◽  
Pattern Search ◽  
Case Based Reasoning ◽  
Turbine Engine ◽  
Local Searches ◽  
Case Based ◽  
Engine Rotor

The implementation of an automated decision support system in the field of structural design and optimization can give a significant advantage to any industry working on mechanical design. Such a system can reduce the project cycle time or allow more time to produce a better design by providing solution ideas to a designer or by upgrading existing design solutions while the designer is not at work. This paper presents an approach to automating the process of designing a gas turbine engine rotor disc using case-based reasoning (CBR), combined with a new genetic algorithm, the Genetic Algorithm with Territorial core Evolution (GATE). GATE was specifically created to solve problems in the mechanical structural design field, and is essentially a real number genetic algorithm that prevents new individuals from being born too close to previously evaluated solutions. The restricted area becomes smaller or larger during optimization to allow global or local searches when necessary. The CBR process uses a databank filled with every known solution to similar design problems. The closest solutions to the current problem in terms of specifications are selected, along with an estimated solution from an artificial neural network. Each solution selected by the CBR is then used to initialize the population of a GATE island. Our results show that CBR may significantly upgrade the performance of an optimization algorithm when sufficient preliminary information is known about the design problem. It provides an average solution 5.0% lighter than the average solution found using random initialization. The results are compared to other results obtained for the same problems by four optimization algorithms from the I-SIGHT 3.5 software: the sequential quadratic programming algorithm (SQP), the insular genetic algorithm (GA), the Hookes & Jeeves generalized pattern search (HJ) and POINTER. Results show that GATE can be a very good candidate for automating and accelerating the structural design of a gas turbine engine rotor disc, providing an average disc 18.9% lighter than SQP, 11.2% lighter than HJ, 23.9% lighter than GA and 4.3% lighter than POINTER, even when starting with the same solution set.

scholarly journals Construction Cost Estimation Using a Case-Based Reasoning Hybrid Genetic Algorithm Based on Local Search Method

2020 ◽  
Vol 12 (19) ◽  
pp. 7920
Author(s):  
Sangsun Jung ◽  
Jae-Ho Pyeon ◽  
Hyun-Soo Lee ◽  
Moonseo Park ◽  
Inseok Yoon ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Local Search ◽  
Cost Estimation ◽  
Hybrid Genetic Algorithm ◽  
Construction Cost ◽  
Case Based Reasoning ◽  
Construction Costs ◽  
Initial Stage ◽  
Case Based

Estimates of project costs in the early stages of a construction project have a significant impact on the operator’s decision-making in essential matters, such as the site’s decision or the construction period. However, it is not easy to carry out the initial stage with confidence, because information such as design books and specifications is not available. In previous studies, case-based reasoning (CBR) is used to estimate initial construction costs, and genetic algorithms are used to calculate the weight of the retrieve phase in CBR’s process. However, it is difficult to draw a better solution than the current one, because existing genetic algorithms use random numbers. To overcome these limitations, we reflect correlation numbers in the genetic algorithms by using the method of local search. Then, we determine the weights using a hybrid genetic algorithm that combines local search and genetic algorithms. A case-based reasoning model was developed using a hybrid genetic algorithm. Then, the model was verified with construction cost data that were not used for the development of the model. As a result, it was found that the hybrid genetic algorithm and case-based reasoning applied with the local search performed better than the existing solution. The detail mean error value was found to be 3.52%, 6.15%, and 0.33% higher for each case than the previous one.

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