Rapid Prototype Development Approach for Genetic Programming

Genetic Programming (GP) is an important approach to deal with complex problem analysis and modeling, and has been applied in a wide range of areas. The development of GP involves various aspects, including design of genetic operators, evolutionary controls and implementations of heuristic strategy, evaluations and other mechanisms. When designing genetic operators, it is necessary to consider the possible limitations of encoding methods of individuals. And when selecting evolutionary control strategies, it is also necessary to balance search efficiency and diversity based on representation characteristics as well as the problem itself. More importantly, all of these matters, among others, have to be implemented through tedious coding work. Therefore, GP development is both complex and time-consuming. To overcome some of these difficulties that hinder the enhancement of GP development efficiency, we explore the feasibility of mutual assistance among GP variants, and then propose a rapid GP prototyping development method based on πGrammatical Evolution (πGE). It is demonstrated through regression analysis experiments that not only is this method beneficial for the GP developers to get rid of some tedious implementations, but also enables them to concentrate on the essence of the referred problem, such as individual representation, decoding means and evaluation. Additionally, it provides new insights into the roles of individual delineations in phenotypes and semantic research of individuals.

The Role and Place of Artificial Neural Network Architectures Structural Redundancy in the Input Data Prototypes and Generalization Development

Neural Networks (NN) are the functional unit of Deep Learning and are known to mimic the behavior of the human brain to solve complex data-driven problems. Whenever we train our own neural networks, we need to take care of something called the generalization of the neural network. The performance of Artificial Neural Networks (ANN) mostly depends upon its generalization capability. In this paper, we propose an innovative approach to enhance the generalization capability of artificial neural networks (ANN) using structural redundancy. A novel perspective on handling input data prototypes and their impact on the development of generalization, which could improve to ANN architectures accuracy and reliability is described.

Prototype of China's Mars Rover Under Smooth Development

Mars orbiting,landing and roving are expected to be achieved during China’s first Mars exploration mission,disclosed at the working meeting of the rover system for the mission held on March 1,2017.The mission,covers a complicated process,requiring large technical leaps,many critical links and huge engineering challenges,is of great significance in promoting advancement of space technology and

Improving Quality of Education by Evaluating the Capacity of Lecturers Using a Web Based System

Evaluating the capacity of lecturer is the key to improve quality of education by improving lecturer capacity in higher education institution. Lecturer’s capacity has been evaluated using different parameters in Assosa University, Ethiopia. Mandatorily, lecturers are evaluated using printed check list. For the last few years we observed that, the lecturer efficiency score is found on the shelf and not checked by anyone to know the gaps and to establish follow-up system. The use of intranet based web system is better to use lecturer’s efficiency result to monitor their performance and to establish follow-up mechanism. In this study, a prototype of web based yearbook efficiency management system for evaluating and monitoring the performance of lecturers was designed and developed. The evaluation process in the system was presented according to the university evaluation format. The output generated by the proposed system can be used by lecturers, HoD, HRM and academic managers to monitor teaching performance.