Improving Generalisation of Genetic Programming for Symbolic Regression with Angle-Driven Geometric Semantic Operators

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@Article{Chen:ieeeTEC:8462796,

• author = "Qi Chen and Bing Xue and Mengjie Zhang",
• title = "Improving Generalisation of Genetic Programming for Symbolic Regression with Angle-Driven Geometric Semantic Operators",
• journal = "IEEE Transactions on Evolutionary Computation",
• year = "2019",
• volume = "23",
• number = "3",
• pages = "488--502",
• month = jun,
• keywords = "genetic algorithms, genetic programming, Geometric Semantic Operator, Symbolic Regression, Generalisation",
• ISSN = "1089-778X",
• DOI = "doi:10.1109/TEVC.2018.2869621",
• size = "15 pages",
• abstract = "Geometric semantic genetic programming has recently attracted much attention. The key innovations are inducing a unimodal fitness landscape in the semantic space and providing a theoretical framework for designing geometric semantic operators. The geometric semantic operators aim to manipulate the semantics of programs by making a bounded semantic impact and generating child programs with similar or better behaviour than their parents. These properties are shown to be highly related to a notable generalisation improvement in genetic programming. However, the potential ineffectiveness and difficulties in bounding the variations in these geometric operators still limits their positive effect on generalisation. This work attempts to further explore the geometry and search space of geometric operators to gain a greater generalisation improvement in genetic programming for symbolic regression. To this end, a new angle-driven selection operator and two new angle-driven geometric search operators are proposed. The angle-awareness brings new geometric properties to these geometric operators, which are expected to provide a greater leverage for approximating the target semantics in each operation, and more importantly, be resistant to over fitting. The experiments show that compared with two state-of-the-art geometric semantic operators, our angle-driven geometric operators not only drive the evolutionary process to fit the target semantics more efficiently but also improve the generalisation performance. A further comparison between the evolved models shows that the new method generally produces simpler models with a much smaller size and is more likely to evolve towards the correct structure of the target models.",
• notes = "also known as \cite{8462796}",

}

Genetic Programming entries for Qi Chen Bing Xue Mengjie Zhang

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