Block-Bench: A Framework for Controllable and Transparent Discrete Optimization Benchmarking

2026-04-08

AI Summary

Existing discrete optimization benchmarks lack fine-grained control over problem characteristics, limiting in-depth analysis of algorithmic behavior. This work proposes a modular benchmark construction framework based on block functions, configurable weights, and dependency graphs, enabling explicit manipulation of problem structure—such as objective space morphology and variable interdependencies—for the first time. The approach facilitates tracking algorithm dynamics at both objective and variable levels and has been successfully applied to analyze the behavior of large-scale, multimodal discrete heuristic algorithms. By offering precise control over problem properties, this framework establishes a new paradigm for research into adaptive mechanisms, diversity management, and dynamic or multi-objective optimization.

Abstract

We present a novel approach for constructing discrete optimization benchmarks that enables fine-grained control over problem properties, and such benchmarks can facilitate analyzing discrete algorithm behaviors. We build benchmark problems based on a set of block functions, where each block function maps a subset of variables to a real value. Problems are instantiated through a set of block functions, weight factors, and an adjacency graph representing the dependency among the block functions. Through analyzing intermediate block values, our framework allows to analyze algorithm behavior not only in the objective space but also at the level of variable representations in the obtained solutions. This capacity is particularly useful for analyzing discrete heuristics in large-scale multi-modal problems, thereby enhancing the practical relevance of benchmark studies. We demonstrate how the proposed approach can inspire the related work in self-adaptation and diversity control in evolutionary algorithms. Moreover, we explain that the proposed benchmark design enables explicit control over problem properties, supporting research in broader domains such as dynamic algorithm configuration and multi-objective optimization.

Problem

• discrete optimization
• benchmarking
• algorithm behavior analysis
• multi-modal problems
• problem properties control

Innovation

• block functions
• discrete optimization benchmarking
• algorithm behavior analysis
• controllable problem properties
• transparent benchmark design