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To improve protein folding simulations, a novel chaotic clonal genetic algorithm (CCGA) was investigated on a 2D lattice model. The novel algorithm combines chaos operator, clonal selection algorithm, and genetic algorithm. We compared CCGA with standard genetic algorithm (SGA) and immune genetic algorithm (IGA) for various chain lengths. It has shown that CCGA not only find global minima more reliably, but also be significantly faster in convergence.