Genetic Algorithm for Optimal Value Computation

In this example, we employ a Genetic Algorithm (GA) to compute the global optimum value of a function. Genetic Algorithm is a heuristic optimization technique that simulates natural selection and genetic mechanisms to search for optimal solutions. The implementation typically involves initializing a population where each individual represents a potential solution encoded as chromosomes (often as binary strings or real-valued vectors). We then evaluate these individuals using a fitness function that quantifies solution quality. Following the evaluation, selection operations (such as tournament selection or roulette wheel selection) identify high-fitness individuals for crossover (recombination) and mutation operations. Crossover combines parental chromosomes to produce offspring, while mutation introduces random variations to maintain genetic diversity. Through iterative generations of selection, crossover, and mutation, the population evolves toward improved solutions. This evolutionary process enables GA to effectively solve complex optimization problems that are challenging for traditional methods, particularly in non-convex or high-dimensional search spaces. Key functions in the implementation include population initialization, fitness calculation, genetic operators, and convergence checking to terminate the algorithm when optimal criteria are met.