H3AD: A hybrid hyper-heuristic for algorithm design

Designing an algorithm to solve a given problem is a challenging task due to the variety of possible design choices and the lack of clear guidelines on how to choose and/or combine them. Optimization and machine learning techniques have been used to make the algorithm design process more independent on human intervention. Hyper-heuristic approaches, in particular, have been proposed to search the space of algorithms/heuristics and/or their components, and iteratively combine and adapt them for specific problems. Although flexible to produce customized algorithms, hyper-heuristics can be extremely costly procedures. This paper proposes a novel hybrid hyper-heuristic (H3AD), which combines an automated algorithm selection approach with a generative hyper-heuristic. This combination intends to reduce the cost of providing an algorithm for a new input problem by reusing algorithms previously built by hyper-heuristics to solve similar problems. H3AD was evaluated in a case study to optimize the design of Particle Swarm Optimization algorithms in unconstrained continuous optimization problems. The results showed that H3AD provided appropriate recommendations of algorithms, reusing the algorithms generated by the hyper-heuristic to new input problems. Besides, H3AD drastically reduced the time of providing a customized algorithm when compared to generative hyper-heuristics, without a significant loss of optimization performance.