Subtree semantic geometric crossover for genetic programming

被引:0
|
作者
Quang Uy Nguyen
Tuan Anh Pham
Xuan Hoai Nguyen
James McDermott
机构
[1] Le Quy Don Technical University,Faculty of IT
[2] Military Academy of Logistics,Institute of IT
[3] Hanoi University,IT Research Centre
[4] University College Dublin,Lochlann Quinn School of Business
关键词
Genetic programming; Semantics; Geometric crossover; Symbolic regression;
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学科分类号
摘要
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.
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页码:25 / 53
页数:28
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