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A new polygonal approximation algorithm, employing the concept of genetic evolution, is presented. In the proposed method, a chromosome is used to represent a polygon by a binary string. Each bit, called a gene, represents a point on the given curve. Three genetic operators, including selection, crossover, and mutation, are designed to obtain the approximated polygon whose error is bounded by a given norm. Many experiments show that the convergence is guaranteed and the optimal or near-optimal solutions can be obtained. Compared with the Zhu–Seneviratne algorithm,²⁴ the proposed algorithm successfully reduced the number of segments under the same error condition in the polygonal approximation.

Multiple sequence alignment (MSA) is one of the basic and important problems in molecular biology. MSA can be used for different purposes including finding the conserved motifs and structurally important regions in protein sequences and determine evolutionary distance between sequences. Aligning several sequences cannot be done in polynomial time and therefore heuristic methods such as genetic algorithms can be used to find approximate solutions of MSA problems. Several algorithms based on genetic algorithms have been developed for this problem in recent years. Most of these algorithms use very complicated, problem specific and time consuming mutation operators. In this paper, we propose a new algorithm that uses a new way of population initialization and simple mutation and recombination operators. The strength of the proposed GA is using simple mutation operators and also a special recombination operator that does not have problems of similar recombination operators in other GAs. The experimental results show that the proposed algorithm is capable of finding good MSAs in contrast to existing methods, while it uses simple operators with low computational complexity.

Adaptive Resonance Theory (ART) neural network architectures, such as Fuzzy ARTMAP (FAM), have solved successfully a variety of classification problems. However, FAM suffers from an inherent problem that of creating larger architectures than it is necessary to solve the problem at hand (referred to as the ART category proliferation problem). This problem is especially amplified for classification problems which have noisy data, and/or data, belonging to different labels, that significantly overlap. In this paper we introduce m-GFAM (modified genetically engineered Fuzzy ARTMAP), which is produced by evolving a population of FAM architectures. Our results demonstrate that m-GFAM successfully addresses the category proliferation problem by creating a small size trained ART structure that exhibits good generalization. Our experiments show that m-GFAM outperforms other ART architectures that have addressed the category proliferation problem before.