Surrogate-Assisted Genetic Programming with Rank-Based Phenotypic Characterisation for Dynamic Multi-Mode Project Scheduling

• URL: http://arxiv.org/abs/2603.16286v1
• Date: Tue, 17 Mar 2026 09:19:20 GMT
• Title: Surrogate-Assisted Genetic Programming with Rank-Based Phenotypic Characterisation for Dynamic Multi-Mode Project Scheduling
• Abstract summary: Genetic Programming (GP) has been shown to evolve rules for decision-making tasks.<n>GP requires problem-specific phenotypic characterisation (PC) schemes of rules.<n>This paper proposes a rank-driven PC scheme derived from ordering of eligible activity-mode pairs and activity groups in decision situations.<n>The resulting PC vectors enable a surrogate model to estimate the fitness of unevaluated GP individuals.
• Score: 6.835074198877901
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The dynamic multi-mode resource-constrained project scheduling problem (DMRCPSP) is of practical importance, as it requires making real-time decisions under changing project states and resource availability. Genetic Programming (GP) has been shown to effectively evolve heuristic rules for such decision-making tasks; however, the evolutionary process typically relies on a large number of simulation-based fitness evaluations, resulting in high computational cost. Surrogate models offer a promising solution to reduce evaluation cost, but their application to GP requires problem-specific phenotypic characterisation (PC) schemes of heuristic rules. There is currently a lack of suitable PC schemes for GP applied to DMRCPSP. This paper proposes a rank-based PC scheme derived from heuristic-driven ordering of eligible activity-mode pairs and activity groups in decision situations. The resulting PC vectors enable a surrogate model to estimate the fitness of unevaluated GP individuals. Based on this scheme, a surrogate-assisted GP algorithm is developed. Experimental results demonstrate that the proposed surrogate-assisted GP can identify high-quality heuristic rules consistently earlier than the state-of-the-art GP approach for DMRCPSP, while introducing only marginal computational overhead. Further analyses demonstrate that the surrogate model provides useful guidance for offspring selection, leading to improved evolutionary efficiency.

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