<span>This paper presents the optimization of house purchase recommendation system (HRPS) using Genetic Algorithm. Everyone in this world has their own dream house and plan to purchase it depending on their budget and based on the house preferences. Homebuyers face problem in comparing the house property websites according to the factors during the house survey. Subsequently, it is time-consuming in making the decision and they need to bear the transportation cost as they will need to travel to one house developer office to another. In addition, some of the homebuyers felt disappointed when their expectations were not met. Thus, in order to optimize the preferences, in this paper, we present a web-based house purchase recommendation system (HPRS) using a genetic algorithm. Then, it follows by test the functionality and usability of the system. As a result, the system found to function accordingly and obtain more than the average score of system usability scale testing. For further research, it is recommended to add more data to the database and compare with other algorithms.</span>
Metaheuristic approaches are the most selected technique to find optimization solution intelligently in many areas of timetabling and scheduling, space allocation, decision making and others. These approaches have promised a better solution in single objective optimization problem. However, there is no revealed discussion on the issue that has more than one problem. In agricultural land use planning, we found that there are two related problems need to be solved intelligently before obtaining the main objective of optimal solution for the land. The problems are i) to allocate the resources into agricultural land optimally, then ii) to arrange the plant in the planting area in order to find an optimal layout. The solution of the both problems will utilize the land and consequently maximize number of plant to be planted in an area respectively. This paper is preliminary investigation towards optimizing agricultural land, in order that we focus on the understanding of the issues in agricultural land and solution methods by referring to the similarity of the previous researches. We also promote the solutions idea and show the complexity of the problems, and finally find that the metaheuristic approaches are a necessity.
The purpose in shape assignment is to find the optimal solution that combines a number of shapes with attention to full use of area. To achieve this, an analysis needs to be done several times because of the different solutions produce dissimilar number of items. Although to find the optimal solution is a certainty, the ambiguity matters and huge possible solutions require an intelligent approach to be applied. Genetic Algorithm (GA) was chosen to overcome this problem. We found that basic implementation of Genetic Algorithm produces uncertainty time and most probably contribute the longer processing time with several reasons. Thus, in order to reduce time in analysis process, we improved the Genetic Algorithm by focusing on 1) specific-domain initialization that gene values are based on the X and Y of area coordinate 2) the use of short term memory to avoid the revisit solutions occur. Through a series of experiment, the repetition of time towards obtaining the optimal result using basic GA (BGA) and improved GA (IGA) gradually increase when size of area of combined shapes raise. Using the same datasets, however, the BGA shows more repetition number than IGA indicates that IGA spent less computation time.
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