Adaptive search heuristics are known to be valuable in approximating solutions to hard search problems. However, these techniques are problem dependent. Inspired by the idea of life cycle stages found in nature, we introduce a hybrid approach called the LifeCycle model that simultaneously applies genetic algorithms (GAs), particle swarm optimisation (PSOs), and stochastic hill climbing to create a generally well-performing search heuristics. In the LifeCycle model, we consider candidate solutions and their fitness as individuals, which, based on their recent search progress, can decide to become either a GA individual, a particle of a PSO, or a single stochastic hill climber. First results from a comparison of our new approach with the single search algorithms indicate a generally good performance in numerical optimization.