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This paper surveys strategies applied to avoid premature convergence in Genetic Algorithms (GAs). Genetic Algorithm belongs to the set of nature inspired algorithms. The applications of GA cover wide domains such as optimization, pattern recognition, learning, scheduling, economics, bioinformatics, etc. Fitness function is the measure of GA, distributed randomly in the population. Typically, the particular value for each gene start dominating as the search evolves. During the evolutionary search, fitness decreases as the population converges, this leads to the problems of the premature convergence and slow finishing. In this paper, a detailed and comprehensive survey of different approaches implemented to prevent premature convergence with their strengths and weaknesses is presented. This paper also discusses the details about GA, factors affecting the performance during the search for global optima and brief details about the theoretical framework of Genetic algorithm. The surveyed research is organized in a systematic order. A detailed summary and analysis of reviewed literature are given for the quick review. A comparison of reviewed literature has been made based on different parameters. The underlying motivation for this paper is to identify methods that allow the development of new strategies to prevent premature convergence and the effective utilization of genetic algorithms in the different area of research.
- Evolutionary algorithmsGenetic algorithmMarkov chainPremature convergenceSchema theoryStatistical mechanics