This paper presents a Hybrid Clonal Selection based method for generating healthy meals as starting from a given user request, a diet recommendation, and a set of food offers. The method proposed is based on a hybrid model, which consists of one core component and two hybridization components. The core component uses the CLONAG algorithm. One of the hybridization components is based on flower pollination, whereas the other utilizes tabu search and reinforcement learning. The flower pollination component is used for modifying the generated clones, while the tabu search and reinforcement learning component aims to improve the search capabilities of the core component by means of long-term and short-term memory structures. We integrated our method into an experimental prototype and we evaluated it on different older adult profiles.