GA-TSP Genetic Algorithm: Optimization Techniques and Code Implementation

In this article, we explore fundamental applications and advantages of genetic algorithms (GA). Genetic algorithms represent a computational intelligence approach that utilizes principles of natural selection and genetic inheritance to solve optimization problems. This method has been widely applied across various domains including engineering, biology, economics, and artificial intelligence. A primary application involves finding optimal solutions in optimization problems, which can be implemented across numerous engineering and scientific fields.

For instance, in machine learning implementations, genetic algorithms can optimize neural network architectures through chromosome encoding of network parameters (like layer sizes and activation functions) and fitness evaluation based on validation accuracy. In biological simulations, GA can model gene evolution processes using selection, crossover, and mutation operations on population genomes. Additionally, genetic algorithms find applications in engineering design parameter optimization (through iterative fitness-based selection) and robotic path planning (where paths are encoded as gene sequences). The algorithm typically involves key functions: population initialization, fitness calculation, tournament selection, crossover operations (e.g., single-point crossover), and mutation mechanisms. As a powerful optimization tool, genetic algorithms continue to play significant roles in future scientific research, particularly in complex problem spaces where traditional methods face limitations.