👉 Efficient Algorithms (EA) math, often associated with Genetic Algorithms (GAs), is a computational paradigm inspired by natural selection and genetics. It involves using mechanisms like selection, crossover (recombination), and mutation to evolve a population of candidate solutions towards an optimal or near-optimal solution for a given problem. In EAs, a population of potential solutions (individuals) is maintained and iteratively improved through these operations. Each individual is represented by a set of parameters (genes), and the fitness function evaluates how well each solution performs. Selection favors better-performing individuals, crossover combines parts of their genetic material to create offspring, and mutation introduces random changes to maintain diversity. This process mimics biological evolution, allowing EAs to efficiently explore large solution spaces and find good solutions in complex optimization problems where traditional methods may struggle.
