Genetic Programming with Mixed Integer Linear Programming Based Library Search

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@Article{Huynh:ieeeTEC,

• author = "Quang Nhat Huynh and Shelvin Chand and Hemant Kumar Singh and Tapabrata Ray",
• title = "Genetic Programming with Mixed Integer Linear Programming Based Library Search",
• journal = "IEEE Transactions on Evolutionary Computation",
• year = "2018",
• volume = "22",
• number = "5",
• pages = "733--747",
• month = oct,
• keywords = "genetic algorithms, genetic programming, Mixed Integer Linear Programming, MLIP, Semantic Backpropagation, SB, Library of Sub-expressions",
• ISSN = "1089-778X",
• URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8364611",
• DOI = "doi:10.1109/TEVC.2018.2840056",
• size = "15 pages",
• abstract = "Genetic programming (GP) is one of the commonly used tools for symbolic regression. In the field of GP, the use of semantics and an external library of sub-expressions for designing better search operators has recently gained significant attention. A notable example is semantic back-propagation, which has demonstrated an ability to obtain expressions with extremely small prediction errors. However, these expressions often tend to be long and difficult to interpret, which may restrict their applicability in real-life problems. In this paper, we propose a GP framework that includes two key elements, a new library construction scheme and a novel semantic operator based on mixed-integer linear programming (MILP). The proposed library construction scheme maintains diverse sub-expressions and keeps the library size in check by imposing an upper limit. The proposed semantic operator constructs new expressions by effectively combining a given number of sub-expressions from the library. These improvements have been integrated in a bi-objective GP framework with random desired operator (RDO), which attempts to simultaneously reduce the complexity and improve the fitness of the evolving expressions. The contributions of individual components are studied in detail using fifteen benchmarks. It is observed that the use of the proposed scheme with RDO leads to shorter expressions without sacrificing accuracy of approximation. The addition of MILP further improves the results for certain types of problems.",
• notes = "also known as \cite{8364611}",

}

Genetic Programming entries for Quang Nhat Huynh Shelvin Chand Hemant Kumar Singh Tapabrata Ray

Citations