Subtree semantic geometric crossover for genetic programming

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@Article{Nguyen:2016:GPEM,

• author = "Quang Uy Nguyen and Tuan Anh Pham and Xuan Hoai Nguyen and James McDermott",
• title = "Subtree semantic geometric crossover for genetic programming",
• journal = "Genetic Programming and Evolvable Machines",
• year = "2016",
• volume = "17",
• number = "1",
• pages = "25--53",
• month = mar,
• keywords = "genetic algorithms, genetic programming, Semantics, Geometric crossover, Symbolic regression",
• ISSN = "1389-2576",
• DOI = "doi:10.1007/s10710-015-9253-5",
• size = "29 pages",
• abstract = "The semantic geometric crossover (SGX) proposed by Moraglio et al. has achieved very promising results and received great attention from researchers, but has a significant disadvantage in the exponential growth in size of the solutions. We propose a crossover operator named subtree semantic geometric crossover (SSGX), with the aim of addressing this issue. It is similar to SGX but uses subtree semantic similarity to approximate the geometric property. We compare SSGX to standard crossover (SC), to SGX, and to other recent semantic-based crossover operators, testing on several symbolic regression problems. Overall our new operator out-performs the other operators on test data performance, and reduces computational time relative to most of them. Further analysis shows that while SGX is rather exploitative, and SC rather explorative, SSGX achieves a balance between the two. A simple method of further enhancing SSGX performance is also demonstrated.",

}

Genetic Programming entries for Quang Uy Nguyen Pham Tuan Anh Nguyen Xuan Hoai James McDermott

Citations