From Genes to Memes: Optimization by Problem-aware Evolutionary Algorithms
نویسنده
چکیده
Memetic algorithms are population-based metaheuristics aimed to solve hard optimization problems. These techniques are explicitly concerned with exploiting available knowledge in order to achieve the most effective resolution of the target problem. The rationale behind this optimization philosophy, namely the intrinsic theoretical limitations of problem-unaware optimization techniques, is presented in this work. A glimpse of the main features of memetic algorithms, and a brief overview of the numerous applications of these techniques is provided as well.
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