A Genetic Algorithm Using Triplet Nucleotide Encoding and DNA Reproduction Operations for Unconstrained Optimization Problems

Abstract

As one of the evolutionary heuristics methods, genetic algorithms (GAs) have shown a promising ability to solve complex optimization problems. However, existing GAs still have difficulties in finding the global optimum and avoiding premature convergence. To further improve the search efficiency and convergence rate of evolution algorithms, inspired by the mechanism of biological DNA genetic information and evolution, we present a new genetic algorithm, called GA-TNE+DRO, which uses a novel triplet nucleotide coding scheme to encode potential solutions and a set of new genetic operators to search for globally optimal solutions. The coding scheme represents potential solutions as a sequence of triplet nucleotides and the DNA reproduction operations mimic the DNA reproduction process more vividly than existing DNA-GAs. We compared our algorithm with several existing GA and DNA-based GA algorithms using a benchmark of eight unconstrained optimization functions. Our experimental results show that the proposed algorithm can converge to solutions much closer to the global optimal solutions in a much lower number of iterations than the existing algorithms. A complexity analysis also shows that our algorithm is computationally more efficient than the existing algorithms.

A Novel Double-Strand DNA Genetic Algorithm for Multi-Objective Optimization

“…Designed based on the splicing model [30], the crossover operator takes the upper-strand of two individuals, creates a complementary strand [14] for each upper-strand, cuts each pair of double strands into four pieces, and paste the pieces back in a crossover fashion. For example, suppose CCCTCGACCCCC and AAAGCG-CAAAAA are the upper-strands of two individuals.…”

“…In a closely related research area, DNA-based genetic algorithms (DNA-GAs) [14]- [17] were proposed to solve complex optimization problems. These algorithms extend conventional genetic algorithms [18] and DNA computing [19], both of which have been widely used in solving complex problems [20], [21] by mimicking the genetic mechanisms in the nature.…”

Neural Network Evolving Algorithm Based on the Triplet Codon Encoding Method

“…More and more researchers are becoming interested in simulating the DNA genetic mechanism in computation [28,29,30,31,32], and in the field of neural evolution, a variety of network encoding strategies have emerged. The direct encoding method has been able to realize the common evolution of network topology and weights, but the existing research results of indirect encoding are still limited by the theoretical basis, and the development is not mature.…”

“…Assuming the middle three triplet codons in a component are mapped to an integer sequence as {bit1,bit2,bit3}, the connection weight of that component w can be calculated as B=∑j=1k(bitj)×19l/3−j w=B(wmax−wmin)19l/3−1+wmin where B is an intermediate variable, while Equation (4) is used to map the value of the intermediate variable B to weight w [32].…”

A Kernel-Based Intuitionistic Fuzzy C-Means Clustering Using a DNA Genetic Algorithm for Magnetic Resonance Image Segmentation

“…The DNA genetic algorithm, based on DNA computing [22] and the Genetic Algorithm (GA) [23], have been recently introduced to solve complex optimization problems in many areas, such as, chemical engineering process parameter estimation [24], function optimization [25], clustering analysis [26,27], and membrane computation [28]. This technique can be used to solve the aforementioned optimization problem.…”