On the Problem Characteristics of Multi-objective Pseudo-Boolean Functions in Runtime Analysis
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912470730276864 |
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| author | Liang, Zimin Li, Miqing |
| author_facet | Liang, Zimin Li, Miqing |
| contents | Recently, there has been growing interest within the theoretical community in analytically studying multi-objective evolutionary algorithms. This runtime analysis-focused research can help formally understand algorithm behaviour, explain empirical observations, and provide theoretical insights to support algorithm development and exploration. However, the test problems commonly used in the theoretical analysis are predominantly limited to problems with heavy "artificial" characteristics (e.g., symmetric, homogeneous objectives and linear Pareto fronts), which may not be able to well represent realistic scenarios. In this paper, we first discuss commonly used multi-objective functions in the theory domain and systematically review their features, limitations and implications to practical use. Then, we present several new functions with more realistic features, such as heterogenous objectives, local optimality and nonlinearity of the Pareto front, through simply mixing and matching classical single-objective functions in the area (e.g., LeadingOnes, Jump and RoyalRoad). We hope these functions can enrich the existing test problem suites, and strengthen the connection between theoretic and practical research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_19166 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On the Problem Characteristics of Multi-objective Pseudo-Boolean Functions in Runtime Analysis Liang, Zimin Li, Miqing Neural and Evolutionary Computing Recently, there has been growing interest within the theoretical community in analytically studying multi-objective evolutionary algorithms. This runtime analysis-focused research can help formally understand algorithm behaviour, explain empirical observations, and provide theoretical insights to support algorithm development and exploration. However, the test problems commonly used in the theoretical analysis are predominantly limited to problems with heavy "artificial" characteristics (e.g., symmetric, homogeneous objectives and linear Pareto fronts), which may not be able to well represent realistic scenarios. In this paper, we first discuss commonly used multi-objective functions in the theory domain and systematically review their features, limitations and implications to practical use. Then, we present several new functions with more realistic features, such as heterogenous objectives, local optimality and nonlinearity of the Pareto front, through simply mixing and matching classical single-objective functions in the area (e.g., LeadingOnes, Jump and RoyalRoad). We hope these functions can enrich the existing test problem suites, and strengthen the connection between theoretic and practical research. |
| title | On the Problem Characteristics of Multi-objective Pseudo-Boolean Functions in Runtime Analysis |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2503.19166 |