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Can Mutations Replace Local Search? Studying the Effect of Repeated Genetic Programming Operators in the Unrelated Machines Environment

  • 2026
  • OriginalPaper
  • Chapter
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Abstract

This chapter delves into the effectiveness of genetic programming operators in solving scheduling problems within the unrelated machines environment. The study contrasts the impact of repeated mutations and crossovers against traditional local search methods. Key findings reveal that repeated mutations can achieve similar performance improvements as local search, while repeated crossovers yield inferior results. The experimental design involves a comprehensive analysis of various evolutionary schemes, including repeated crossover, repeated mutation, and local search models. Results indicate that repeated mutations offer a balanced trade-off between exploration and exploitation, enhancing the evolutionary process without the need for complex local search operators. The chapter concludes that repeating genetic operators can significantly improve algorithm performance, providing a simpler and more efficient approach to optimizing scheduling algorithms.

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Title
Can Mutations Replace Local Search? Studying the Effect of Repeated Genetic Programming Operators in the Unrelated Machines Environment
Authors
Josip Hrvatić
Marko Đurasević
Francisco Javier Gil-Gala
Domagoj Jakobović
Copyright Year
2026
DOI
https://doi.org/10.1007/978-3-032-07998-5_11
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