Genetic algorithm and pure random search for exosensor distribution optimisation

2012 ◽  
Vol 4 (6) ◽  
pp. 359 ◽  
Author(s):  
Michael P. Poland ◽  
Christopher D. Nugent ◽  
Hui Wang ◽  
Liming Chen
Keyword(s):  
Genetic Algorithm ◽  
Random Search ◽  
Pure Random Search

scholarly journals Performance of Enhanced Multiple-Searching Genetic Algorithm for Test Case Generation in Software Testing

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1779
Author(s):  
Wanida Khamprapai ◽  
Cheng-Fa Tsai ◽  
Paohsi Wang ◽  
Chi-En Tsai
Keyword(s):  
Genetic Algorithm ◽  
Software Testing ◽  
Random Search ◽  
Automatic Generation ◽  
Superior Performance ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Network Systems ◽  
Routing Problem

Test case generation is an important process in software testing. However, manual generation of test cases is a time-consuming process. Automation can considerably reduce the time required to create adequate test cases for software testing. Genetic algorithms (GAs) are considered to be effective in this regard. The multiple-searching genetic algorithm (MSGA) uses a modified version of the GA to solve the multicast routing problem in network systems. MSGA can be improved to make it suitable for generating test cases. In this paper, a new algorithm called the enhanced multiple-searching genetic algorithm (EMSGA), which involves a few additional processes for selecting the best chromosomes in the GA process, is proposed. The performance of EMSGA was evaluated through comparison with seven different search-based techniques, including random search. All algorithms were implemented in EvoSuite, which is a tool for automatic generation of test cases. The experimental results showed that EMSGA increased the efficiency of testing when compared with conventional algorithms and could detect more faults. Because of its superior performance compared with that of existing algorithms, EMSGA can enable seamless automation of software testing, thereby facilitating the development of different software packages.

Solving Machine Loading Problem of FMS

2011 ◽  
pp. 19-43 ◽  
Author(s):  
Anoop Prakash ◽  
Nagesh Shukla ◽  
Ravi Shankar ◽  
Manoj Kumar Tiwari
Keyword(s):  
Genetic Algorithm ◽  
Ant Colony Optimization ◽  
Flexible Manufacturing ◽  
Manufacturing Systems ◽  
Random Search ◽  
Search Algorithms ◽  
Ant Colony ◽  
Complex Nature ◽  
Machine Loading ◽  
Loading Problem

Artificial intelligence (AI) refers to intelligence artificially realized through computation. AI has emerged as one of the promising computer science discipline originated in mid-1950. Over the past few decades, AI based random search algorithms, namely, genetic algorithm, ant colony optimization, and so forth have found their applicability in solving various real-world problems of complex nature. This chapter is mainly concerned with the application of some AI based random search algorithms, namely, genetic algorithm (GA), ant colony optimization (ACO), simulated annealing (SA), artificial immune system (AIS), and tabu search (TS), to solve the machine loading problem in flexible manufacturing system. Performance evaluation of the aforementioned search algorithms have been tested over standard benchmark dataset. In addition, the results obtained from them are compared with the results of some of the best heuristic procedures in the literature. The objectives of the present chapter is to make the readers fully aware about the intricate solutions existing in the machine loading problem of flexible manufacturing systems (FMS) to exemplify the generic procedure of various AI based random search algorithms. Also, the present chapter describes the step-wise implementation of search algorithms over machine loading problem.

scholarly journals Genetic Algorithm: Reviews, Implementations, and Applications

2020 ◽  
Vol 10 (6) ◽  
pp. 57
Author(s):  
Tanweer Alam ◽  
Shamimul Qamar ◽  
Amit Dixit ◽  
Mohamed Benaida
Keyword(s):  
Genetic Algorithm ◽  
Natural Selection ◽  
Behavioral Science ◽  
Random Search ◽  
Statistical Investigation ◽  
Adaptive Technology ◽  
Mutation Operators ◽  
Engineering Pedagogy ◽  
Crossover And Mutation ◽  
Investigation Process

Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as adaptive technology to learn and solve complex problems and issues. It is a meta-heuristic approach that is used to solve hybrid computation challenges. GA utilizes selection, crossover, and mutation operators to effectively manage the searching system strategy. This algorithm is derived from natural selection and genetics concepts. GA is an intelligent use of random search supported with historical data to contribute the search in an area of the improved outcome within a coverage framework. Such algorithms are widely used for maintaining high-quality reactions to optimize issues and problems investigation. These techniques are recognized to be somewhat of a statistical investigation process to search for a suitable solution or prevent an accurate strategy for challenges in optimization or searches. These techniques have been produced from natural selection or genetics principles. For random testing, historical information is provided with intelligent enslavement to continue moving the search out from the area of improved features for processing of the outcomes. It is a category of heuristics of evolutionary history using behavioral science-influenced methods like an annuity, gene, preference, or combination (sometimes refers to as hybridization). This method seemed to be a valuable tool to find solutions for problems optimization. In this paper, the author has explored the GAs, its role in engineering pedagogies, and the emerging areas where it is using, and its implementation.

Approximate Implementations of Pure Random Search in the Presence of Noise

2005 ◽  
Vol 31 (4) ◽  
pp. 601-612 ◽  
Author(s):  
David L. J. Alexander ◽  
David W. Bulger ◽  
James M. Calvin ◽  
H. Edwin. Romeijn ◽  
Ryan L. Sherriff
Keyword(s):  
Random Search ◽  
Pure Random Search

Adaptive Random Search with Intensification and Diversification Combined with Genetic Algorithm

2006 ◽  
Vol 10 (6) ◽  
pp. 921-930 ◽  
Author(s):  
Dongkyu Sohn ◽  
◽  
Hiroyuki Hatakeyama ◽  
Shingo Mabu ◽  
Kotaro Hirasawa ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Random Search ◽  
Optimization Method ◽  
Objective Functions ◽  
Adaptive Random Search ◽  
Searching Ability

A novel optimization method named RasID-GA (an abbreviation of Adaptive Random Search with Intensification and Diversification combined with Genetic Algorithm) is proposed in order to enhance the searching ability of conventional RasID, which is a kind of Random Search with Intensification and Diversification. In this paper, the timing of switching from RasID to GA, or from GA to RasID is also studied. RasID-GA is compared with parallel RasIDs and GA using 23 different objective functions, and it turns out that RasID-GA performs well compared with other methods.

Application of an Improved Genetic Algorithm with the Search Space Compression in TSP

2013 ◽  
Vol 765-767 ◽  
pp. 687-689
Author(s):  
Yi Song ◽  
Ni Ni Wei
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Random Search ◽  
Search Space ◽  
Traveling Salesman ◽  
Improved Genetic Algorithm ◽  
Space Compression ◽  
Accelerate Convergence ◽  
The Traveling Salesman Problem ◽  
Exponential Relation

The Traveling Salesman Problem is a combinatorial optimization problem, the problem has been shown to belong to the NPC problem, the possible solution of Traveling Salesman Problem and the scale of the cities have the exponential relation, so the more bigger of the scale. In this paper, improve the search process of the genetic algorithm by introducing the idea is to compress the search space. The simulation results show that for solving the TSP, the algorithm can quickly obtain multiple high-quality solutions. It can reduce the blindness of random search and accelerate convergence of the algorithm.

Rough Terrain Rovers Dynamics Analysis and Optimization

2005 ◽  
Author(s):  
Hadi Tavakoli Nia ◽  
Seyed Hamidreza Alemohammad ◽  
Saeed Bagheri ◽  
Reza Hajiaghaee Khiabani ◽  
Ali Meghdari
Keyword(s):  
Genetic Algorithm ◽  
Software Package ◽  
Random Search ◽  
Nonlinear Problems ◽  
Directional Derivatives ◽  
Rough Terrain ◽  
Dynamics Analysis ◽  
Search Technique ◽  
Dynamical Equations ◽  
New Approach

In this paper a new approach to dynamics optimization of rough terrain rovers is introduced. Since rover wheels traction has a significant role in rover mobility, optimization is based on the minimization of traction at rover wheel-ground interfaces. The method of optimization chosen is Genetic Algorithm (GA) which is a directed random search technique along with the usual optimization based on directional derivatives. GA is a suitable and efficient method of optimization for nonlinear problems. The procedure is applied on a specific rough terrain rover called CEDRA-I Shrimp Rover. Dynamical equations are obtained using Kane’s method. Finally, the results are verified by modeling of the rover in ADAMS® software package.

scholarly journals Genetic Algorithm – A Sensible Evolutionary Optimization Technique

2019 ◽  
Vol 8 (9) ◽  
pp. 2805-2809
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Random Search ◽  
Optimization Technique ◽  
Search Space ◽  
Effective Population ◽  
Natural Systems ◽  
Heuristic Search Algorithm ◽  
Feasible Solutions

The study presents a pragmatic outlook of genetic algorithm. Many biological algorithms are inspired for their ability to evolve towards best solutions and of all; genetic algorithm is widely accepted as they well suit evolutionary computing models. Genetic algorithm could generate optimal solutions on random as well as deterministic problems. Genetic algorithm is a mathematical approach to imitate the processes studied in natural evolution. The methodology of genetic algorithm is intensively experimented in order to use the power of evolution to solve optimization problems. Genetic algorithm is an adaptive heuristic search algorithm based on the evolutionary ideas of genetics and natural selection. Genetic algorithm exploits random search approach to solve optimization problems. Genetic algorithm takes benefits of historical information to direct the search into the convergence of better performance within the search space. The basic techniques of evolutionary algorithms are observed to be simulating the processes in natural systems. These techniques are aimed to carry effective population to the next generation and ensure the survival of the fittest. Nature supports the domination of stronger over the weaker ones in any kind. In this study, we proposed the arithmetic views of the behavior and operators of genetic algorithm that support the evolution of feasible solutions to optimized solutions.

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