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Unconstrained Search Space in Genetic Algorithms
Genetic algorithms offer flexibility by allowing crossovers between multiple individuals, leading to new combinations and mutations. In a search space with minimal constraints, novel findings can emerge through the application of fitness and selection functions in successive generations. This uniqueness is highlighted by examples such as using an LLM trained on various antenna designs, showcasing the ability to generate novelty beyond simple interpolation.