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This paper proposes a multi-objective genetic programming (MOGP) for automatic construction of feature extraction programs (FEPs). The proposed method is modified from a well known non-dominated sorting evolutionary algorithm, i.e., NSGA-II. The key differences of the method are related with redundancies in program representation. We apply redundancy regulations in three main processes of the MOGP,...
This paper propose a new recombination method, named redundancy-removed recombination, for linear genetic programming based image recognition program synthesis. The redundancy-removed recombination produces an offspring (by conventional crossover or mutation), and then adopts a canonical transformation to convert the offspring into its canonical form, in which it can be verified whether it has been...
Recently, evolutionary computation (EC) has been adopted to search for effective feature extraction programs for given image recognition problems. For this approach, feature extraction programs are constructed from a set of primitive operations (POs), which are usually general image processing and pattern recognition operations. In this paper, we focus on an approach based on a variation of linear...
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