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This paper investigates how genetic algorithms (GAs) can be improved to solve large-scale and complex problems more efficiently. First of all, we review premature convergence, one of the challenges confronted with when applying GAs to real-world problems. Next, some of the methods now available to prevent premature convergence and their intrinsic defects are discussed. A qualitative analysis is then done on the cause of premature convergence that is the loss of building blocks hosted indoi:10.1145/1276958.1277188 dblp:conf/gecco/ChenHHY07 fatcat:wc6yyidcvjhndctjebwzim5hpe