Co-evolving genetic algorithm with filtered evaluation function

As a function optimizer or a search procedure, genetic algorithms (GAs) are very powerful and have many advantages. Fundamental research concerning the internal behavior of GAs has highlighted their limitations as regards the search performances, called GA-hard problems. The reason for these difficulties seems to be that GAs generate insufficient strategies for the convergence of populations. To overcome this problem an extended GA, which we name the filtering-GA, that adopts the concept of co-evolution, is proposed. It has two GAs, and they influence each other through their evaluation process.<
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