@inproceedings{8e9fcf213ca64d86bdcc914a5f878f64,
title = "Genetic algorithms are very good solved Sudoku generators",
abstract = "I present a simple and yet effective GA-based approach to content generation in the Sudoku domain. The GA finds multiple full boards which can act as solutions for Sudoku and Killer Sudoku puzzles. In this work I use a binning-based diversity maintenance approach in order to encourage GA to find more solution boards. resluts prove that though both approaches routinely manage to find multiple solution boards it is in fact the simple GA without any diversity maintenance that finds more such boards. Using a simpler approach to manipulate the fitness function to penalize previously found solutions improves the algorithm further.",
keywords = "Diversity, Evolutionary algorithms, Puzzles, Sudoku",
author = "Amit Benbassat",
note = "Publisher Copyright: {\textcopyright} 2019 Copyright held by the owner/author(s).; 2019 Genetic and Evolutionary Computation Conference, GECCO 2019 ; Conference date: 13-07-2019 Through 17-07-2019",
year = "2019",
month = jul,
day = "13",
doi = "10.1145/3319619.3326793",
language = "English",
series = "GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion",
publisher = "Association for Computing Machinery, Inc",
pages = "49--50",
booktitle = "GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion",
}