Preeti Singh, Chetan Gupta, Kapil Kashyap et al. · 2013
Abstract: Genetic algorithm is very powerful technique to find approximate solution to search problems or patterns through application based on biological terms. Genetic algorithms use biologically inspired techniques such as genetic inheritance, natural selection, mutation, and sexual reproduction (recombination, or crossover. The general strengths of genetic algorithms lie in their ability to explore the search space efficiently through parallel evaluation of fitness and mixing of partial solutions through crossover, maintain a search frontier to seek global optima and solve multi-criterion optimization problems. In this paper, Application based on scheduling static tasks in homogeneous parallel system is discussed. Especially uni-processor system would not be sufficient enough to execute all the tasks parallel so it requires an efficient algorithm to determine when and on which processor a given task should execute A static task can be partitioned into a group of subtasks and represented as a DAG (Directed Acyclic Graph), that problem can be stated as finding a schedule for a DAG to be executed in a parallel multiprocessor system.
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S.W. Lee, Hung-Tat Tsui · 2005
We propose a new relational multi-objective genetic algorithm (RMOGA) with a novel generic operator, inheritance. It is found that inheritance have very good performance when used with a multi-objective genetic algorithm. Its characteristic is similar to a crossover operator. However, it aims to exchange the mathematical relationships, but not values, between two selected sub-chromosomes. We also propose to use a Pareto-ranking to help form a fitness function. Then it is optimized by the relational multi-objective genetic algorithm, which includes the new operator. The advantage of this approach is its ability to inherit the best relationships. To test the effectiveness of the proposed methodology, experiments on using it for assembling broken objects were performed. Excellent results are obtained.
Cited by 1
Tianyu Wei, Khuong Le, Justin Tagalogon · ScienceOpen Research · 2026
Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. They are considered as a search-based technique based on the principles of Genetics and Natural Selection used in computing to find an exact or approximate solution for optimization and search problems. Genetic algorithms are also termed as heuristic search algorithms that belong to the larger part of evolutionary algorithms. They are inspired by evolutionary biology such as selection, cross over, and inheritance mutation. These algorithms provide a technique for the program to automatically improve the class of their parameters and generate high-quality solutions for optimization problems and search problems. This paper is an introduction of the Genetic algorithm approach and briefly describes some of the most interesting research or applications which are used in real-world projects that enable readers to implement and experiment with Genetic Algorithm on their own.
Cited by 0Open access
Khuong Le, Tianyu Wei, Justin Tagalogon · 2023
In the realms of science and engineering, genetic algorithms have established themselves as versatile algorithms, capable of tackling a diverse range of practical problems and serving as computational models that mirror the intricate mechanisms of natural evolutionary systems. They are considered as a search-based technique based on the principles of Genetics and Natural Selection used in computing to find an exact or approximate solution for optimization and search problems. Genetic algorithms are also termed as heuristic search algorithms that belong to the larger part of evolutionary algorithms. They are inspired by evolutionary biology such as selection, cross over, and inheritance mutation. These algorithms provide a technique for the program to automatically improve the class of their parameters and generate high-quality solutions for optimization problems and search problems . This paper is an introduction of the Genetic algorithm approach and briefly describes some of the most interesting research or applications which are used in real-world projects that enable readers to implement and experiment with Genetic Algorithm on their own.
Cited by 40Open access
Esther Lugo-González, Emmanuel A. Merchán-Cruz, Luis H. Hernández-Gómez et al. · Contact-less Assessment of In-vivo Body Signals Using Microwave Doppler Radar (InTech) · 2012
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Arindam Banerjee · Smart innovation, systems and technologies · 2024
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Esther Lugo-Gonzlez, Emmanuel Aguilar, Henrique Luís et al. · Greater South Information System · 2012
Genetic Algorithms are a search paradigm that applies principles of evolutionary biology (crossover, mutation, natural selection) in order to deal with intractable search spaces. The power and success of GA are mostly achieved by the diversity with the individuals of a population which evolve, in parallel, following the principle of the survival of the fittest. In general, the genetic algorithms resolve combinatorial optimization problems that in (Goldberg, 1989) are mentioned, this implies a large number of responses associated with an exponential growth in solutions potentially feasible according to the magnitude of the problem. In a standard GA the diversity of the individuals is obtained and maintained using the genetic operators crossover and mutation which allow the GA to find feasible solutions and avoid premature convergence to a local maximum (Holland, 1975).
Cited by 0Open access
Yifei Liu, Yankun Gao, Yang Liu · 2022
The traditional Genetic Algorithm is not only easy to fall into the local optimal solution, but also has poor stability. In response to this problem, I. Harvey proposed a Microbial Genetic Algorithm. Using the idea of microbial inheritance, the process of selection and crossover in the classical genetic algorithm is replaced by means of confrontation and infection. This algorithm can retain better parent population, and save those genes. However, this algorithm works well for small populations, and because only one new offspring is generated per confrontation, the efficiency is the problem. Aiming at this problem, an Improved Microbial Genetic Algorithm (IMGA) is proposed, which transforms random confrontation into ordered group confrontation with roulette selection algorithm. The simulation results of the TSP problem show that the new algorithm is suitable for large populations and the efficiency is also improved. Both in terms of time efficiency and the results of the optimal solution, IMGA is better than GA and MGA.
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Yinan Guo, Dunwei Gong, Zhengui Xue · 2006
Knowledge about evolutionary information is not used in genetic algorithms effectively. Cultural algorithms with dual inheritance structure converge slowly because only mutation operator is adopted in the population space. A novel hybrid optimization method is proposed using genetic algorithm in population space. Four kinds of knowledge and two phases are abstracted. Steps of the algorithm are described in detail. Simulation results on the benchmark optimization functions indicate that the method converges faster than traditional cultural algorithms. In iteratively dynamic situation, results show that experience knowledge in the knowledge space is benefit to apperceive the change of situation and has the ability in memory, which increases the speed of convergence in a certain situation.
Cited by 9
J. M. Edelstein · Spectrum Research Repository (Concordia University) · 2001
Over the past 25 years, a new form of optimization and search technique was refined using the same theories developed to explain human evolution, called the Genetic Algorithm. The algorithm is based on the premise that, when individuals exist in a competitive environment where resources are limited, only the fittest individuals will survive. This thesis attempts to use genetic algorithms to solve a complex optimization problem, which involves a real world simulation where trucks compete to make a profit, by buying and selling commodities in a country filled with different types of dealers. The genetic algorithm is used to evolve the trucks over generations, by modifying the strategies that they use to control their behaviour, in an attempt to produce more profitable trucks. The project was implemented using the C++ programming language. The purpose of the thesis and its implementation was to see if one could use object oriented techniques, such as inheritance and dynamic binding to achieve the genetic variation of the trucks. The results of the project are moderately successful. This is likely due number of strategies available to the trucks. By increasing the numb algorithm may produce trucks of superior genetic structure.
Cited by 0Open access
Jiangming Mao, Kotaro Hirasawa, Jinglu Hu et al. · IEEJ Transactions on Electronics Information and Systems · 2003
Genetic algorithms are often well suited for optimization problems because of their parallel searching and evolutionary ability. Crossover and mutation are believed to be the main exploration operators. In this paper, we focus on how crossover and mutation work in binary-coded genetic algorithm and investigate their effects on bit’s frequency of population. According to the analysis of equilibrium of crossover, we can see the bit-based simulated crossover (BSC) is strong crossover method. Furthermore, to increase robustness of binary-coded genetic algorithm, multi-generation inheritance evolutionary strategy(MGIS) was proposed. Simulation results demonstrate the effectiveness of the proposed method.
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Na Zhao · 2024
Genetic algorithm is an optimization algorithm that simulates the process of biological evolution. It is based on natural selection and genetic evolution theory, and searches for the optimal solution of a problem by simulating the crossover and mutation of chromosome genes. In genetic algorithms, the problem-solving process is transformed into chromosome gene operations similar to those in biological evolution, thereby achieving optimized solutions for complex problems. In the process of digitizing ethnic and folk oral literature, genetic algorithms have wide applicability. Firstly, oral literature often contains rich cultural connotations and unique artistic forms, which need to be accurately and completely preserved and inherited in the process of digitization. Genetic algorithm can effectively process a large amount of oral literature data by simulating the process of biological evolution, extract key information and features, and achieve accurate representation and transmission of oral literature content.
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Yin Zhe · Open Journal of Genetics · 2012
In this paper, based on binomial formula, a law of genetic inheritance is proposed, and it would have certain reference value for future genetic inheritance research. Besides, a recursive algorithm for recessive inheritance is also proposed. Given any two of the male parent, female parent and filial generation, the genomic information of the third one can be calculated uniquely.
Cited by 4Open access
Vellore Tamilnadu India · 2010
SAbstract: A genetic algorithm (GA) is a search technique used in computing to find exact or approximate solutions to optimization and search problems. Genetic algorithms are a particular class of evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance, mutation, selection and crossover. GA is a method for search and optimization based on the process of natural selection and evolution. In this approach, several modifications are done for effective implementation of GA to solve the Electric Power Service Restoration Problem. The GA is suitable for the supply restoration because it is very easy to change constraints or objectives, or apply new ones. The objective function includes all the objectives and constraints required for a practical supply restoration scheme. GA starts with number of solutions to a problem, encoded as a string of status of sectionalizing and tie switches. The status of the switch ‘1 ’ and ‘0 ’ has been considered as ‘close ’ and ‘open ’ condition of the switch. The string that encodes each string is ‘chromosome ’ and the set of solutions are termed as population. 1.
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Pratibha Bajpai · 2013
The genetic algorithm (GA) is a search heuristic that is routinely used to generate useful solutions to optimization and search problems. It generates solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. Genetic algorithms are one of the best ways to solve a problem for which little is known. They are a very general algorithm and so work well in any search space. All you need to know is what you need the solution to be able to do well, and a genetic algorithm will be able to create a high quality solution.
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Xiaomei Zheng, Dongyang Li, Lei Wang et al. · 2017
The genetic algorithm (GA) uses techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover. Due to its ability to find approximate solutions of hard optimization, the GA is suited for applications in various domains. The GA-based composition system, which uses genetic algorithm to evolve music materials, has become a hot spot in the algorithmic composition. This paper discusses Chinese folk composition based on GA. A structure of GA-based Chinese folk composition system is presented. Evolutionary melody operators for Chinese folk music are proposed and the fitness functions are discussed in detail. Experimental results show that the model based on GA is feasible and effective in the process of Chinese folk music composition.
Cited by 12
Kornel Chromiński, Magdalena Tkacz · Procedia Computer Science · 2021
The article presents a new operator in the genetic algorithm. The proposed operator mimics the epigenetic process of prion inheritance. For living organisms, epigenetic processes have a large impact on the differentiation of the population, hence the idea to imitate these processes in genetic algorithms. For the purposes of the experiments, a genetic algorithm to detect outliers was used. The proposed operator mimics the epigenetic process was added to the basic genetic algorithm and the impact of the operator on the effectiveness of the algorithm was assessed. The impact of the proposed modification on the efficiency of the genetic algorithm was tested on six data sets. The article also presents an experimental analysis of the proposed operator.
Cited by 0Open access
Himma Firdaus, Tri Widianti · 2024
Genetic Algorithms (GAs) are famous for solving real-world problems despite a tendency to converge prematurely. This paper introduces the Royal Lineage Genetic Algorithm (RoLiGA), a GA enhancement inspired by chromosomal inheritance in royal lineages. The RoLiGA employed several algorithms, including the insertion heuristic, elitist domination procedure, fitness-based-roulette selection, multi-prince family's regeneration, partially mapped crossover, repetitive 2-point swap mutation, and 2-opt heuristics. The combination ensures the chromosomal inheritance process leads to optimal solutions. Tested on 26 Traveling Salesman Problem (TSP) datasets, RoLiGA outperforms five GA-based hybrid algorithms, obtaining the best-known values and setting new benchmarks for solution quality.
Cited by 1
Kim Hong Soon, Patricia Anthony, Jason Teo et al. · InTech eBooks · 2012
Genetic algorithm is one of the successful optimization algorithm used in computing to find exact or approximate solutions for certain complex problems. This novel algorithm was first introduced by John Holland in 1975 (Holland, 1975). Besides Holland, many other researchers have also contributed to genetic algorithm (Davis, 1987; Davis, 1991; Grefenstte, 1986; Goldberg, 1989; Michalewicz, 1992). This is an algorithm that imitates the evolutionary process concept based on the Darwinian Theory which emphasizes on the law of “the survival of the fittest”. This algorithm used techniques which are inspired from evolution biology such as inheritance, selection, crossover and mutation (Engelbrecht, 2002).
Cited by 1Open access
Yijun Yang · 2010
In order to reduce the power loss,a diagnostic strategy based genetic algorithm for distribution network reconfiguration is proposed in this paper.A new coding precept for genetic diagnosis strategy is introduced to improve the inheritance operation.The high-quality genes for diagnosing are deposited in gene banks;and the inferior genes(infeasible solution) are corrected by opening up the loops and connecting the islands.Simulations for the typical IEEE testing systems reconfiguraltion are carried out,and the proposed algorithm are compared with the traditional quick branch-exchange algorithm.Results show that the proposed algorithm has validity and effectiveness for reducing power loss.
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