Limiting the Number of Fitness Cases in Genetic Programming Using Statistics

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{giacobini:ppsn2002:pp371,

• author = "Mario Giacobini and Marco Tomassini and Leonardo Vanneschi",
• title = "Limiting the Number of Fitness Cases in Genetic Programming Using Statistics",
• booktitle = "Parallel Problem Solving from Nature - PPSN VII",
• address = "Granada, Spain",
• month = "7-11 " # sep,
• pages = "371--380",
• year = "2002",
• editor = "Juan J. Merelo-Guervos and Panagiotis Adamidis and Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and Hans-Paul Schwefel",
• number = "2439",
• series = "Lecture Notes in Computer Science",
• publisher = "Springer-Verlag",
• keywords = "genetic algorithms, genetic programming, Parameter tuning, Fitness Evaluation, Theory of evolutionary computing, Central Limit Theorem, Entropy",
• ISBN = "3-540-44139-5",
• DOI = "doi:10.1007/3-540-45712-7_36",
• URL = "https://rdcu.be/cJz75",
• size = "10 pages",
• abstract = "Fitness evaluation is often a time consuming activity in genetic programming applications and it is thus of interest to find criteria that can help in reducing the time without compromising the quality of the results. We use well-known results in statistics and information theory to limit the number of fitness cases that are needed for reliable function reconstruction in genetic programming. By using two numerical examples, we show that the results agree with our theoretical predictions. Since our approach is problem-independent, it can be used together with techniques for choosing an efficient set of fitness cases.",

}

Genetic Programming entries for Mario Giacobini Marco Tomassini Leonardo Vanneschi

Citations