MATLAB Implementation of Genetic Algorithm with Floating-Point Encoding and Adaptive Operators

In the genetic algorithm implementation, we employ floating-point encoding to represent genes. Floating-point encoding provides a wider range and higher precision compared to binary encoding, enabling the representation of more potential solutions in continuous search spaces. The implementation typically involves creating chromosome structures as arrays of floating-point numbers, where each element corresponds to a decision variable in the optimization problem. Furthermore, we incorporate adaptive crossover and mutation factors to enhance the algorithm's search capabilities. The adaptive mechanism dynamically adjusts these operators based on population diversity or fitness progression. For crossover, we might implement arithmetic crossover or simulated binary crossover (SBX) where the crossover probability adapts according to generation count or fitness variance. The mutation operator could use Gaussian mutation or polynomial mutation with adaptive step sizes. Through this adaptive adjustment of operators, the algorithm can dynamically modify the intensity of crossover and mutation during the search process, allowing it to better adapt to problem complexity and characteristics. This adaptive approach typically involves monitoring convergence metrics and adjusting operator parameters accordingly using conditional statements or mathematical functions. This methodology enables more comprehensive exploration of the search space and facilitates finding superior solutions. The implementation would include functions for population initialization, fitness evaluation, selection (using techniques like tournament selection or roulette wheel), adaptive operator application, and elitism preservation to maintain the best solutions across generations.