Improving Controllability of Chaotic Landscape Generators by Property Evolution

As optimization algorithms progress to address more complex and high-dimensional challenges, the need for benchmark problems that are both diverse and controllable has become crucial for effective performance evaluation. However, traditional benchmark problem generators often fall short in capturing the required diversity and controllability, limiting their effectiveness in assessing algorithm performance. This paper introduces a novel Controllable Chaotic Landscape Generator (CCLG), designed to enhance the controllability of generated landscapes through the integration of optimization techniques, while maintaining high diversity. This study leverages common problem attributes from the BBOB benchmark suite as targets, enabling effective control over both local and global characteristics of the generated problems, such as the positions of local optima, condition numbers, ruggedness, and global structure. Experimental results demonstrate that CCLG not only achieves effective control over landscape features but also preserves high diversity to meet various optimization requirements.