MATLAB Implementation of Enhanced Genetic Algorithm

Genetic Algorithm (GA) is an intelligent optimization algorithm that simulates natural selection and genetic mechanisms, widely applied in function optimization, engineering design, machine learning, and other fields. While traditional genetic algorithms are simple and effective, they suffer from limitations in convergence speed and susceptibility to local optima. Enhanced genetic algorithms significantly improve performance by optimizing selection, crossover, and mutation operations.

When implementing enhanced genetic algorithms in MATLAB, key optimizations typically focus on the following aspects: Fitness Function Design: Appropriately designed fitness functions facilitate better evaluation of individual quality and help avoid local optima. In MATLAB, this involves defining a function that returns scalar fitness values, often normalized or scaled to maintain selection pressure. Selection Mechanism Enhancement: Strategies like roulette wheel selection, tournament selection, or elitism preservation increase population diversity. MATLAB implementations commonly use functions like `selectiontournament` or custom selection routines that prioritize high-fitness individuals while maintaining diversity. Crossover and Mutation Optimization: Adaptive adjustment of crossover and mutation rates enhances global and local search capabilities. MATLAB code typically implements this through conditional statements that modify rates based on population diversity metrics or generation count, using functions like `crossoverintermediate` or custom mutation operators. Population Initialization: Methods such as chaotic mapping or Latin hypercube sampling improve initial population diversity. MATLAB's `lhsdesign` function or chaotic sequence generators can create well-distributed initial populations covering the search space more effectively than random initialization.

The enhanced genetic algorithm implemented in MATLAB demonstrates higher efficiency, better balancing global exploration and local exploitation, making it suitable for complex optimization problems. Further improvements in convergence speed and precision can be achieved through parameter tuning and operator optimization, such as implementing dynamic parameter adjustment based on population statistics or incorporating local search techniques within the GA framework.