Proposal of Multimodal Program Optimization Benchmark and Its Application to Multimodal Genetic Programming

Multimodal program optimizations (MMPOs) have been studied in recent years. MMPOs aims at obtaining multiple optimal programs with different structures simultaneously. This paper proposes novel MMPO benchmark problems to evaluate the performance of the multimodal program search algorithms. In particular, we propose five MMPOs, which have different characteristics, the similarity between optimal programs, the complexity of optimal programs, and the number of local optimal programs. We apply multimodal genetic programming (MMGP) proposed in our previous work to the proposed MMPOs to verify their difficulty and effectiveness, and evaluate the performance of MMGP. The experimental results reveal that the proposed MMPOs are difficult and complex to obtain the global and local optimal programs simultaneously as compared to the conventional benchmark. In addition, the experimental results clarify mechanisms to improve the performance of MMGP.