Reducing Bloat in GP with Multiple Objectives
Natural Computing Series, ISSN: 1619-7127, Page: 177-200
2008
- 15Citations
- 37Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Book Chapter Description
This chapter investigates the use of multiobjective techniques in genetic programming (GP) in order to evolve compact programs and to reduce the effects caused by bloating. The underlying approach considers the program size as a second, independent objective besides program functionality, and several studies have found this concept to be successful in reducing bloat. Based on one specific algorithm, we demonstrate the principle of multiobjective GP and show how to apply Pareto-based strategies to GP. This approach outperforms four classical strategies to reduce bloat with regard to both convergence speed and size of the produced programs on an even-parity problem. Additionally, we investigate the question of why the Pareto-based strategies can be more effective in reducing bloat than alternative strategies on several test problems. The analysis falsifies the hypothesis that the small but less functional individuals that are kept in the population act as building blocks for larger correct solutions. This leads to the conclusion that the advantages are probably due to the increased diversity in the population.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85128837456&origin=inward; http://dx.doi.org/10.1007/978-3-540-72964-8_9; https://link.springer.com/10.1007/978-3-540-72964-8_9; https://dx.doi.org/10.1007/978-3-540-72964-8_9; https://link.springer.com/chapter/10.1007/978-3-540-72964-8_9; http://www.springerlink.com/index/10.1007/978-3-540-72964-8_9; http://www.springerlink.com/index/pdf/10.1007/978-3-540-72964-8_9
Springer Science and Business Media LLC
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know