Data Abstraction for Cognitive Models of Compositional Design in Genetic Algorithms
نویسندگان
چکیده
In this paper, we discuss the design of suites of equally valid optimization strategies for job scheduling using genetic algorithms. Our initial designs will be neutral in the sense that none of our choices lead to job schedules which when implemented would be described by qualities such as contented, antagonistic or demoralized. The data will provide training examples for meta level associative cortex and abstract emotion modules. There are two goals to this research: first, model the process of optimization design for eventual use in the development of an autonomous optimization scheduling program which is capable of using intangible qualities as part of the design process; and second, use the optimization model as a quantitative means of training the associative cortex portion and associated emotional circuits of a general model of cognition. ∗Department of Industrial Engineering, email: [email protected] †Department of Mathematical Sciences, email: [email protected]
منابع مشابه
Pareto Optimization of Two-element Wing Models with Morphing Flap Using Computational Fluid Dynamics, Grouped Method of Data handling Artificial Neural Networks and Genetic Algorithms
A multi-objective optimization (MOO) of two-element wing models with morphing flap by using computational fluid dynamics (CFD) techniques, artificial neural networks (ANN), and non-dominated sorting genetic algorithms (NSGA II), is performed in this paper. At first, the domain is solved numerically in various two-element wing models with morphing flap using CFD techniques and lift (L) and drag ...
متن کاملA New Hybrid model of Multi-layer Perceptron Artificial Neural Network and Genetic Algorithms in Web Design Management Based on CMS
The size and complexity of websites have grown significantly during recent years. In line with this growth, the need to maintain most of the resources has been intensified. Content Management Systems (CMSs) are software that was presented in accordance with increased demands of users. With the advent of Content Management Systems, factors such as: domains, predesigned module’s development, grap...
متن کاملAERO-THERMODYNAMIC OPTIMIZATION OF TURBOPROP ENGINES USING MULTI-OBJECTIVE GENETIC ALGORITHMS
In this paper multi-objective genetic algorithms were employed for Pareto approach optimization of turboprop engines. The considered objective functions are used to maximize the specific thrust, propulsive efficiency, thermal efficiency, propeller efficiency and minimize the thrust specific fuel consumption. These objectives are usually conflicting with each other. The design variables consist ...
متن کاملSpatiotemporal Estimation of PM2.5 Concentration Using Remotely Sensed Data, Machine Learning, and Optimization Algorithms
PM 2.5 (particles <2.5 μm in aerodynamic diameter) can be measured by ground station data in urban areas, but the number of these stations and their geographical coverage is limited. Therefore, these data are not adequate for calculating concentrations of Pm2.5 over a large urban area. This study aims to use Aerosol Optical Depth (AOD) satellite images and meteorological data from 2014 to 2017 ...
متن کاملOptimization of sediment rating curve coefficients using evolutionary algorithms and unsupervised artificial neural network
Sediment rating curve (SRC) is a conventional and a common regression model in estimating suspended sediment load (SSL) of flow discharge. However, in most cases the data log-transformation in SRC models causing a bias which underestimates SSL prediction. In this study, using the daily stream flow and suspended sediment load data from Shalman hydrometric station on Shalmanroud River, Guilan Pro...
متن کامل