scholarly journals Modifying Regeneration Mutation and Hybridising Clonal Selection for Evolutionary Algorithms Based Timetabling Tool

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Thatchai Thepphakorn ◽  
Pupong Pongcharoen ◽  
Chris Hicks
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Clonal Selection ◽  
Fractional Factorial ◽  
Hill Climbing ◽  
Clonal Selection Algorithm ◽  
Scheduling Problems ◽  
Genetic Operators ◽  
Course Scheduling ◽  
Elitist Strategy

This paper outlines the development of a new evolutionary algorithms based timetabling (EAT) tool for solving course scheduling problems that include a genetic algorithm (GA) and a memetic algorithm (MA). Reproduction processes may generate infeasible solutions. Previous research has used repair processes that have been applied after a population of chromosomes has been generated. This research developed a new approach which (i) modified the genetic operators to prevent the creation of infeasible solutions before chromosomes were added to the population; (ii) included the clonal selection algorithm (CSA); and the elitist strategy (ES) to improve the quality of the solutions produced. This approach was adopted by both the GA and MA within the EAT. The MA was further modified to include hill climbing local search. The EAT program was tested using 14 benchmark timetabling problems from the literature using a sequential experimental design, which included a fractional factorial screening experiment. Experiments were conducted to (i) test the performance of the proposed modified algorithms; (ii) identify which factors and interactions were statistically significant; (iii) identify appropriate parameters for the GA and MA; and (iv) compare the performance of the various hybrid algorithms. The genetic algorithm with modified genetic operators produced an average improvement of over 50%.

Application of Improved Genetic Algorithm to Traffic Equipment’s Rush-Repair in the Wartime

2012 ◽  
Vol 605-607 ◽  
pp. 49-52
Author(s):  
Geng Sheng Wu ◽  
Qi Yi Zhang
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Initial Population ◽  
Scheduling Problems ◽  
Improved Genetic Algorithm ◽  
Genetic Operators ◽  
Assignment Model ◽  
Practical Engineering ◽  
Poor Stability

Traffic equipment’s rush-repairs in the wartime optimal assignment model was established. Combining the features of Job-shop scheduling problems, described the complexity of this problem. In order to find global optimal results efficiently, traditional GAs were improved and used for study of this problem. Though genetic algorithm, as an effective global search method, had been used in many problems, it had the disadvantages of slow convergence and poor stability in practical engineering. In order to overcome these problems, an improved genetic algorithm was proposed in terms of creation of the initial population, genetic operators, etc. At the end, the steps to solve the optimal model were put forward. With this model we had obtained ideal results. This shows that the method can offer a scientific and effective support for a decision maker in command automation of the traffic equipment’s rush-repairs in battlefield.

Efficient Power Control and Clustering Method Based on Hybrid Clonal Genetic Algorithm in Wireless Sensor Networks

2012 ◽  
Vol 263-266 ◽  
pp. 889-897
Author(s):  
Xiang Xian Zhu ◽  
Su Feng Lu
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Large Scale ◽  
Clonal Selection ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Clonal Selection Algorithm ◽  
Clustering Methods

Wireless sensor networks (WSNs) lifetime for large-scale surveillance systems is defined as the time span that all targets can be covered. How to manage the combination of the sensor nodes efficiently to prolong the whole network’s lifetime while insuring the network reliability, it is one of the most important problems to research in WSNs. An effective optimization framework is then proposed, where genetic algorithm and clonal selection algorithm are hybridized to enhance the searching ability. Our goal can be described as minimizing the number of active nodes and the scheduling cost, thus reducing the overall energy consumption to prolong the whole network’s lifetime with certain coverage rate insured. We compare the proposed algorithm with different clustering methods used in the WSNs. The simulation results show that the proposed algorithm has higher efficiency and can achieve better network lifetime and data delivery at the base station.

scholarly journals Penerapan Metode Hybrid Genetic Algorithm (GA) dan Pattern Search (PS) untuk Penjadwalan Mata Kuliah Universitas

2020 ◽  
Vol 9 (3) ◽  
pp. 201-212
Author(s):  
Fani Puspitasari ◽  
Parwadi Moengin
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Pattern Search ◽  
Local Optimum ◽  
Scheduling Problems ◽  
Course Scheduling ◽  
Fitness Value ◽  
Optimum Solution ◽  
University Course

The problem of university course scheduling is a complicated job to do because of the many constraints that must be considered, such as the number of courses, the number of rooms available, the number of students, lecturer preferences, and time slots. The more courses that will be scheduled, the scheduling problem becomes more complex to solve. Therefore, it is necessary to set an automatic course schedule based on optimization method. The aim of this research is to gain an optimal solution in the form of schedule in order to decrease the number of clashed courses, optimize room utilization and consider the preferences of lecturer-course. In this research, a hybridization method of Genetic Algorithm (GA) and Pattern Search (PS) is investigated for solving university course scheduling problems. The main algorithm is GA to find the global optimum solution, while the PS algorithm is used to find the local optimum solution that is difficult to obtain by the GA method. The simulation results with 93 courses show that the Hybrid GA-PS method works better than does the GA method without hybrid, as evidenced by the better fitness value of the hybrid GA-PS method which is -3528.62 and 99.24% of the solutions achieved. While the GA method without hybrid is only able to reach a solution of around 65% and has an average fitness value of -3100.76.

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