A Comprehensive Investigation of Genetic Algorithms

Abstract

Genetic Algorithm (GA) is a population-based stochastic optimizer that mimics natural selection to solve complex, non-differentiable, and multimodal problems. This paper first reviews the biological foundations and canonical operators- selection, crossover and mutation--before surveying major variants such as Hybrid GA, Parallel GA and the Predator--Prey Dynamic GA that inject local search, island parallelism or ecological balance to mitigate premature convergence. This paper then benchmarks these algorithms on continuous function optimisation and combinatorial tasks, using the sphere function and the travelling-salesman problem as illustrative cases. Results show that Hybrid GA with simulated-annealing local search achieves the best accuracy--speed trade-off, while PDGA offers the most stable evolution curve in high-dimensional multimodal landscapes. Finally, this paper identifies remaining challenges--expensive fitness evaluation, parameter sensitivity and interpretability--and outline future directions including surrogate-assisted evolution, GPU/quantum acceleration and lifelong on-device adaptation. The study confirms that GA, when properly hybridised and parallelised, remains a competitive backbone for next-generation AI systems.