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.
Highlights
Optimization problems arise in many real-world applications and various types of intelligent evolutionary optimization methods, such as the Genetic Algorithm (GA) [1], the Particle SwarmOptimization (PSO) [2], the Differential Evolution (DE) [3], and the artificial bee colony (ABC) [4], have been proposed in the literature over the last few decades
We present a new GA, called GA-triplet nucleotide encoding (TNE)+DRO, which uses a novel triplet nucleotide coding scheme to encode individuals of GA and provides a set of novel genetic operators that mimic
Our experimental results show that our algorithm can converge to solutions much closer to the global optimal solutions in a much lower number of iterations than the existing algorithms
Summary
Optimization problems arise in many real-world applications and various types of intelligent evolutionary optimization methods, such as the Genetic Algorithm (GA) [1], the Particle Swarm. The conventional GA encodes a possible solution in the solution space as a string of binary bits which is treated as (a chromosome of) an individual It explores the search space for optimal solutions by generating new individuals from an individual in the current population, using a set of operations that mimic genetic mutation in natural evolution. This approach has shown some success in solving complex optimization problems. DNA computing, proposed by Adleman [11] in 1994, may potentially provide a promising method for solving complex optimization problems because its parallel computing may efficiently search through a large space for potential solutions [12].
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