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- W1978369726 abstract "Genetic Algorithms (GAs) have long been recognized as powerful tools for optimization of complex problems where traditional techniques do not apply. However, although the convergence of elitist GAs to a global optimum has been mathematically proven, the number of iterations remains a case-by-case parameter. We address the problem of determining the best GA out of a family of structurally different evolutionary algorithms by solving a large set of unconstrained functions. We selected 4 structurally different genetic algorithms and a non-evolutionary one (NEA). A schemata analysis was conducted further supporting our claims. As the problems become more demanding, the GAs significantly and consistently outperform the NEA. A particular breed of GA (the Eclectic GA) is superior to all other, in all cases." @default.
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- W1978369726 date "2013-01-01" @default.
- W1978369726 modified "2023-10-16" @default.
- W1978369726 title "The Best Genetic Algorithm I" @default.
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- W1978369726 doi "https://doi.org/10.1007/978-3-642-45111-9_1" @default.
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