Empirical Evaluation of Hill Climbing Algorithm

2017 ◽  
Vol 8 (4) ◽  
pp. 27-40 ◽  
Author(s):  
Manju Khari ◽  
Prabhat Kumar
Keyword(s):  
Collective Behavior ◽  
Empirical Evaluation ◽  
Optimal Solution ◽  
Research Community ◽  
Hill Climbing ◽  
Test Cases ◽  
Average Percentage ◽  
Hill Climbing Algorithm ◽  
Steepest Ascent ◽  
Better Than

The software is growing in size and complexity every day due to which strong need is felt by the research community to search for the techniques which can optimize test cases effectively. The current study is inspired by the collective behavior of finding paths from the colony of food and uses different versions of Hill Climbing Algorithm (HCA) such as Stochastic, and Steepest Ascent HCA for the purpose of finding a good optimal solution. The performance of the proposed algorithm is verified on the basis of three parameters comprising of optimized test cases, time is taken during the optimization process, and the percentage of optimization achieved. The results suggest that proposed Stochastic HCA is significantly average percentage better than Steepest Ascent HCA in reducing the number of test cases in order to accomplish the optimization target.

Search for Prioritized Test Cases during Web Application Testing

2019 ◽  
Vol 10 (2) ◽  
pp. 1-26 ◽  
Author(s):  
Munish Khanna ◽  
Naresh Chauhan ◽  
Dilip Kumar Sharma
Keyword(s):  
Web Application ◽  
Web Applications ◽  
Greedy Algorithms ◽  
Regression Testing ◽  
Test Sequence ◽  
Test Case ◽  
Test Cases ◽  
Average Percentage ◽  
Heuristic Approaches ◽  
Better Than

Regression testing of evolving software is a critical constituent of the software development process. Due to resources constraints, test case prioritization is one of the strategies followed in regression testing during which a test case that satisfies predefined objectives the most, as the tester perceives, would be executed the earliest. In this study, all the experiments were performed on three web applications consisting of 65 to 100 pages with lines of code ranging from 5000 to 7000. Various state-of-the-art approaches such as, heuristic approaches, Greedy approaches, and meta heuristic approaches were applied so as to identify the prioritized test sequence which maximizes the value of average percentage of fault detection. Performance of these algorithms was compared using different parameters and it was concluded that the Artificial Bee Colony algorithm performs better than all. Two novel greedy algorithms are also proposed in the study, of which the goal is to smartly manage the state of a tie, where a tie exhibits the condition that all the test cases participating in the tie are of equal significance in achieving the objective. It has also been validated that the performance of these novel proposed algorithm(s) is better than that of traditionally followed greedy approach, most of the time.

scholarly journals Hierarchical System Decomposition Using Genetic Algorithm for Future Sustainable Computing

2020 ◽  
Vol 12 (6) ◽  
pp. 2177
Author(s):  
Jun-Ho Huh ◽  
Jimin Hwa ◽  
Yeong-Seok Seo
Keyword(s):  
Genetic Algorithm ◽  
Software Architecture ◽  
Large Scale ◽  
Structural Characteristics ◽  
Optimal Solution ◽  
Hill Climbing ◽  
Software Systems ◽  
Small Scale ◽  
Hill Climbing Algorithm ◽  
Architecture Management

A Hierarchical Subsystem Decomposition (HSD) is of great help in understanding large-scale software systems from the software architecture level. However, due to the lack of software architecture management, HSD documentations are often outdated, or they disappear in the course of repeated changes of a software system. Thus, in this paper, we propose a new approach for recovering HSD according to the intended design criteria based on a genetic algorithm to find an optimal solution. Experiments are performed to evaluate the proposed approach using two open source software systems with the 14 fitness functions of the genetic algorithm (GA). The HSDs recovered by our approach have different structural characteristics according to objectives. In the analysis on our GA operators, crossover contributes to a relatively large improvement in the early phase of a search. Mutation renders small-scale improvement in the whole search. Our GA is compared with a Hill-Climbing algorithm (HC) implemented by our GA operators. Although it is still in the primitive stage, our GA leads to higher-quality HSDs than HC. The experimental results indicate that the proposed approach delivers better performance than the existing approach.

Solving the tool switching problem with memetic algorithms

2011 ◽  
Vol 26 (2) ◽  
pp. 221-235 ◽  
Author(s):  
Jhon Edgar Amaya ◽  
Carlos Cotta ◽  
Antonio J. Fernández-Leiva
Keyword(s):  
Flexible Manufacturing ◽  
Manufacturing Systems ◽  
Ad Hoc ◽  
Memetic Algorithm ◽  
Memetic Algorithms ◽  
Hill Climbing ◽  
Beam Search ◽  
Steepest Ascent ◽  
Tool Switching ◽  
Better Than

AbstractThe tool switching problem (ToSP) is well known in the domain of flexible manufacturing systems. Given a reconfigurable machine, the ToSP amounts to scheduling a collection of jobs on this machine (each of them requiring a different set of tools to be completed), as well as the tools to be loaded/unloaded at each step to process these jobs, such that the total number of tool switches is minimized. Different exact and heuristic methods have been defined to deal with this problem. In this work, we focus on memetic approaches to this problem. To this end, we have considered a number of variants of three different local search techniques (hill climbing, tabu search, and simulated annealing), and embedded them in a permutational evolutionary algorithm. It is shown that the memetic algorithm endowed with steepest ascent hill climbing search yields the best results, performing synergistically better than its stand-alone constituents, and providing better results than the rest of the algorithms (including those returned by an effective ad hoc beam search heuristic defined in the literature for this problem).

scholarly journals IMPLEMENTASI ALGORITMA HILL CLIMBING PADA PENENTUAN JARAK TERPENDEK KOTA WISATA DI INDONESIA

2019 ◽  
Vol 1 (3) ◽  
pp. 127-132
Author(s):  
Desti Fitriati ◽  
Nura Meutia Nessrayasa
Keyword(s):  
Optimization Problems ◽  
Complex Problem ◽  
Hill Climbing ◽  
Hill Climbing Algorithm ◽  
Steepest Ascent ◽  
Shortest Route ◽  
Shortest Distance ◽  
Optimization Function ◽  
The Right

Searching and determining the shortest route is a complex problem, looking for the shortest route from a number of attractions and the distance between attractions. With varying access paths, the shortest route search becomes the right choice using a website-based app that provides the closest route on a map using the SAHC (Steepest Ascent Hill Climbing) algorithm. Steepest Ascent Hill Climbing is a method of an algorithm that is widely used for optimization problems. One application is to find the shortest route by maximizing or minimizing the value of the existing optimization function. In research ii study using 34 provinces in Indonesia and every province, there are 5 most popular tour, accuracy value obtained in research determination of the shortest distance of tourist city in Indonesia is 93,3%.  

scholarly journals Courses timetabling based on hill climbing algorithm

2020 ◽  
Vol 10 (6) ◽  
pp. 6558
Author(s):  
Abdoul Rjoub
Keyword(s):  
Optimal Solution ◽  
Initial Solution ◽  
Hill Climbing ◽  
Soft Constraints ◽  
Hill Climbing Algorithm ◽  
Hard Constraints ◽  
Side Constraints ◽  
Time Requirements ◽  
Hard And Soft Constraints ◽  
Hill Climbing Algorithms

In addition to its monotonous nature and excessive time requirements, the manual school timetable scheduling often leads to more than one class being assigned to the same instructor, or more than one instructor being assigned to the same classroom during the same slot time, or even leads to exercise in intentional partialities in favor of a particular group of instructors. In this paper, an automated school timetable scheduling is presented to help overcome the traditional conflicts inherent in the manual scheduling approach. In this approach, hill climbing algorithms have been modified to transact hard and soft constraints. Soft constraints are not easy to be satisfied typically, but hard constraints are obligated. The implementation of this technique has been successfully experimented in different schools with various kinds of side constraints. Results show that the initial solution can be improved by 72% towards the optimal solution within the first 5 seconds and by 50% from the second iteration while the optimal solution will be achieved after 15 iterations ensuring that more than 50% of scientific courses will take place in the early slots time while more than 50% of non-scientific courses will take place during the later time's slots.

scholarly journals Perancangan dan Implementasi Penjadwalan Mata Kuliah Menggunakan Algoritma Steepest Ascent Hill Climbing (Studi Kasus : Fakultas Psikologi UKSW)

d'CARTESIAN ◽  
2013 ◽  
Vol 2 (1) ◽  
pp. 11
Author(s):  
Klaudius Nikotino P ◽  
Magdalena Ineke Pakereng ◽  
Ramos Somya
Keyword(s):  
Teaching And Learning ◽  
Hill Climbing ◽  
Learning Activity ◽  
Scheduling System ◽  
Hill Climbing Algorithm ◽  
Steepest Ascent ◽  
Course Scheduling ◽  
Optimum Solution

Abstract Schedule of courses has been one of the most important part in a university’s teaching and learning activity.  The  large number of  courses and  lecturers  that  are involved,  make some clashes on course’s schedule or class room’s schedule could happens frequently,  so it requires  an application to simplify the process of developing the course schedule. Steepest ascent hill climbing algorithm is an algorithm that generates all possible solutions, and then checks every solution to obtain optimum solution. The result of this research is this course scheduling system can facilitate courses schedulling, in addition  steepest ascent hill climbing algorithm is able to obtain optimum scheduling courses solutions.  Keywords : Course Schedulling, Steepest Ascent Hill Climbing Abstrak Jadwal mata kuliah telah menjadi salah satu bagian terpenting dalam proses belajar mengajar sebuah universitas.  Banyaknya jumlah mata kuliah   dan  dosen yang terlibat, mengakibatkan sering terjadinya bentrokan  jadwal mata kuliah   atau pun dalam pemakaian ruang perkuliahan,  sehingga diperlukan sebuah aplikasi guna mempermudah proses penyusunan jadwal mata kuliah tersebut.  Algoritma  steepest ascent hill climbing  adalah algoritma yang mengumpulkan seluruh solusi yang mungkin, dan men gecek setiap solusi untuk memperoleh solusi yang optimal. Hasil dari penelitian ini adalah aplikasi yang dibangun dapat digunakan untuk memudahkan penjadwalan mata kuliah selain itu algoritma  steepest ascent hill climbing mampu memperoleh solusi penjadwalan mata kuliah yang optimal.   Kata Kunci : Penjadwalan Mata Kuliah, Steepest Ascent Hill Climbing.

scholarly journals Penerapan Metode Steepest Ascent Hill Climb pada Permainan Puzzle

2018 ◽  
Vol 3 (2) ◽  
pp. 36
Author(s):  
Hairul Anam ◽  
Feby Sabilhul Hanafi ◽  
Ahmad Fauzal Adifia ◽  
Ahmad Firdaus Ababil ◽  
Saiful Bukhori
Keyword(s):  
Artificial Intelligence ◽  
Optimization Problems ◽  
Search Algorithms ◽  
Hill Climbing ◽  
Optimal Time ◽  
Hill Climbing Algorithm ◽  
Steepest Ascent ◽  
The Hill

Puzzle is one example of the application of artificial intelligence, in the process of completion there are many search algorithms that can be applied. The 8 puzzle solution will be faster obtained if the array principle is used with a variation of the Steepest-Ascent Hill Climbing (Hill Climbing algorithm by choosing the sharpest / steepest slope) with the correct heuristic parameters and distance heuristics and combined with LogList as the storage state ever passed to overcome the problems in the hill climbing algorithm itself and avoid the looping state that has been passed. Steepest Ascent Hill Climbing is an algorithm method that is widely used for optimization problems. The application of the SAHC (Steepest Ascent Hill Climbing) Algorithm to the puzzle is needed so that the game is completed with optimal time.

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