Publication: Single Fitness Function to Optimize Energy using Genetic Algorithms for Wireless Sensor Network
| dc.contributor.author | Ismail Abdullah | en_US |
| dc.contributor.author | Kalid Abdlkader Marsal | en_US |
| dc.date.accessioned | 2024-05-28T05:49:19Z | |
| dc.date.available | 2024-05-28T05:49:19Z | |
| dc.date.issued | 2017 | |
| dc.description.abstract | A Single fitness function is a particular type of objective function that is used to summarize, as a single figure of merit, how close a given design solution is to achieve the set aims. The Wireless Sensor Network (WSN) has emerged as a promising tool for monitoring the physical world, utilizing self-organizing networks of battery-powered wireless sensors that can sense, process and communicate. A fitness function is used in Genetic Algorithm in each iteration of the algorithm to evaluate the quality of all the proposed solutions to your problem in the current population. The fitness function evaluates how good a single solution in a population is, e.g. if you are trying to find for what x-value a function has it's y-minimum with a Genetic algorithm, the fitness function for a unit might simply be the negative y-value (the smaller is better for fitness function).A reasonable solution to a problem is to investigate a set of solutions, each of which satisfies the objectives at an acceptable level without being dominated by any other solution. GA is a optimization tool, so generally fitness function is a max/min value function consisting of all the variables. If we want to find the best optimal threshold value (i.e. min value of the fitness function), we have to generate a function with these parameters such as Single-to Noise Ratio (SNR), probability of false alarm and number of samples of received data for detection in such a way that the value of the function must be approaching zero. This function is called fitness function and the final value of this function after performing GA will be the optimal outcome. The creation of the function is totally depends on our approach towards the solution of the problem. In this paper, an overview is presented describing single fitness function to optimize energy using genetic algorithms in WSNs. GA are customized to accommodate multi-objective problems by using specialized fitness functions, introducing methods to promote solution diversity, and other approaches. | en_US |
| dc.identifier.epage | 8 | |
| dc.identifier.issue | 1 | |
| dc.identifier.spage | 1 | |
| dc.identifier.uri | https://oarep.usim.edu.my/handle/123456789/6599 | |
| dc.identifier.volume | 1 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Scientific Federation Adobe for Researchers | en_US |
| dc.relation.ispartof | SciFed Journal of Telecommunication | en_US |
| dc.title | Single Fitness Function to Optimize Energy using Genetic Algorithms for Wireless Sensor Network | en_US |
| dc.type | Article | en_US |
| dspace.entity.type | Publication |