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Capacitated arc routing problem (CARP) has attracted much attention due to its generality to many real world problems. Memetic algorithm (MA), among other meta-heuristic search methods, has been shown to achieve competitive performances in solving CARP ranging from small to medium size. In this paper we propose a formal probabilistic memetic algorithm for CARP that is equipped with an adaptation mechanism to control the degree of global exploration against local exploitation while the search progresses. Experimental study on benchmark instances of CARP showed that the proposed probabilistic scheme led to improved search performances when introduced into a recently proposed state-of-the-art MA. The results obtained on 24 instances of the capacitated arc routing problems highlighted the efficacy of the probabilistic scheme with 9 new best known solutions established.