Neuro‐Fuzzy Cost Estimation Model Enhanced by Fast Messy Genetic Algorithms for Semiconductor Hookup Construction

Hsiao, Fan Yi; Wang, Shih Hsu; Wang, Wei‐Chih; Chao, Wen Hung; Yu, Wen

doi:10.1111/j.1467-8667.2012.00786.x

Cited by 58 publications

(38 citation statements)

References 82 publications

(101 reference statements)

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“…Not only is the proposed model able to extract and properly define numerical patterns, but it also deals with discrete domains. Besides, evolutionary ARM proposals usually comprise a huge set of parameters to be tuned, and this task could be a difficult process for non-expert users in evolutionary computation [11,39]. In this regard, the proposed model self-adapts its parameters, which is not an innovation since it is a well-studied area by many researchers [20].…”

Section: Introductionmentioning

confidence: 99%

Reducing gaps in quantitative association rules: A genetic programming free-parameter algorithm

Luna

Romero

et al. 2014

ICA

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The extraction of useful information for decision making is a challenge in many different domains. Association rule mining is one of the most important techniques in this field, discovering relationships of interest among patterns. Despite the mining of association rules being an area of great interest for many researchers, the search for well-grouped continuous values is still a challenge, discovering rules that do not comprise patterns which represent unnecessary ranges of values. Existing algorithms for mining association rules in continuous domains are mainly based on a non-deterministic search, requiring a high number of parameters to be optimised. These parameters hinder the mining process, and the algorithms themselves must be known to those data mining experts that want to use them. We therefore present a grammar guided genetic programming algorithm that does not require as many parameters as other existing approaches and enables the discovery of quantitative association rules comprising small-size gaps. The algorithm is verified over a varied set of data, comparing the results to other association rule mining algorithms from several paradigms. Additionally, some resulting rules from different paradigms are analysed, demonstrating the effectiveness of our model for reducing gaps in numerical features.

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Section: Introductionmentioning

confidence: 99%

Reducing gaps in quantitative association rules: A genetic programming free-parameter algorithm

Luna

Romero

et al. 2014

ICA

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“…Morphological based tools, when combined with evolutionary computation techniques, such as Genetic Algorithms, Genetic Programming as well as Cartesian Genetic Programming [10,17], help to automate the manual process conducted by human experts in many application areas, as described in [2,3,19,27,28,34,40,53,65] and, particularly, in areas related to image processing, with obvious gains in time and efficiency. By combining the two techniques and given a set of morphological operators, the best sequencing of relevant operators for performing a particular task can be automatically found.…”

Section: Introductionmentioning

confidence: 99%

Automatic learning of image filters using Cartesian genetic programming

Paris

Pedrino

Nicoletti

2015

ICA

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This paper proposes a computational modeling for image filtering processes based on the Cartesian Genetic Programming (CGP) methodology, suitable for hardware devices. A computational system named ALIF-CGP (Automatic Learning of Image Filters Using Cartesian Genetic Programming) was designed as a simulator for automatically constructing a sequence of operators, mainly morphological and logical, which can filter a particular shape of image. ALIF-CGP is a convenient option for executing the non-trivial task, usually manually done by human experts, of selecting the sequence of nonlinear operators to be used in morphological filters. ALIF-CGP has already a built-in pool of morphological and logical operators, which can be used by default. The user, however, has the flexibility of choosing only those operators which are of interest or then, conveniently introduce new ones. The system expects as input a pair of images (input-target). The flexibility given by the CGP-based computational modeling used by ALIF-CGP as well as its efficiency and satisfactory results, obtained in various image processing case studies, recommend its use when developing a hardware implementation for the purposes of image filtering. A few case studies using ALIF-CGP are presented and comparatively analyzed in relation to previous results available in the literature.The manual process employed by humans, when dealing with digital image processing, is usually very slow due to the trial and error approach commonly used. The ad-hoc nature of the problem invariably turns the search for the best sequence of image operators into a complex task and its solution unsuitable for reuse.Many available research works published over the past years have focused their efforts trying to reduce the complexity involved in designing new image operators that would be able to perform various computational tasks [8,9,49,50,65]. Particularly, computational tools based on the mathematical morphology formalism are considered the most effective approach when applied in practical and theoretical problems from areas such as image analysis and image processing. Developments in these areas have great importance and impact in subjacent areas such as robotic vision, visual inspection, medicine, analysis of textures, among many others.

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“…Genetic algorithm is a known issue in civil engineering and can easily be combined with other methods. So, it is used in different areas in civil engineering, such as cost estimation [13], design of a longspan suspension bridge [14], life cycle optimization of buildings [15], resource leveling model for line of balance schedules [16], and etc.…”

Section: Introductionmentioning

confidence: 99%

Finding Optimum Resource Allocation to Optimizing Construction Project Time/Cost through Combination of Artificial Agents CPM and GA

Khanzadi

Attar

Bagherpour

2016

Period. Polytech. Civil Eng.

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In order to plan a construction project, computer simulations are frequently used to predict the performance of the operations through simulating the process flows and resource selection procedure. However IntroductionDespite the fact that different robust methods have been developed for project scheduling and planning, in the construction industry, Critical Path Method (CPM) is still the most popular method. The first major cause of this popularity is the simplicity of the CPM. On the other hand, for finding the optimum allocation, planners must test all of the alternate construction technologies and resource allocations, including crew sizes for all of the project activities. In each activity, each of its alternatives cause specific duration and cost. In the complicated projects, with increase in the number of activities and the types of resources, these alternatives increase progressively. Therefore, the effective technique is necessary to analyze and determine the optimal resource allocation to complete a project with the minimum cost or time. Several researchers have used discrete event simulation (DES) to develop such a technique for analyzing the effect of different resource allocations. Examples include evaluating the effect of different resource allocation on the concrete batching operations, [1], earthmoving operations [2], residential construction inspection process [3], precast concrete workshops [4], and tunnel planning [5].Although these simulation techniques could find optimum resource allocation, examination of all resource allocation combinations to determine the best solution is too lengthy. Therefore, sensitivity analysis was proposed by [6] to facilitate such enumerations. However, with increase in the complexity of simulation model and number of available resource combinations, sensitivity analysis becomes an extremely time-consuming process. In this regard, different researchers have used heuristic algorithms (HAs) to efficiently search for appropriate resource allocation under specified objectives. For instance, AbouRizk and Shi (1994) used a heuristic algorithm (HA) to find the best resource allocation in the simulation system [7]. Several researchers proposed a hybrid model with combining genetic algorithms (GAs) and simulation for optimizing resource allocation regarding minimum unit cost or maximum productivity rate of the simulation model [8][9][10]. Senouci and Adeli (2001) used dynamic neural network to optimize resource allocation subjected to project network and available resource constrains that was

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Neuro‐Fuzzy Cost Estimation Model Enhanced by Fast Messy Genetic Algorithms for Semiconductor Hookup Construction

Cited by 58 publications

References 82 publications

Reducing gaps in quantitative association rules: A genetic programming free-parameter algorithm

Reducing gaps in quantitative association rules: A genetic programming free-parameter algorithm

Automatic learning of image filters using Cartesian genetic programming

Finding Optimum Resource Allocation to Optimizing Construction Project Time/Cost through Combination of Artificial Agents CPM and GA

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