{"publication":"Proceedings of the 15th annual conference on Genetic and evolutionary computation","date_created":"2018-12-11T11:59:15Z","month":"07","page":"845 - 852","date_updated":"2021-01-12T06:59:15Z","quality_controlled":"1","_id":"2719","title":"A variance decomposition approach to the analysis of genetic algorithms","department":[{"_id":"NiBa"},{"_id":"CaGu"}],"date_published":"2013-07-01T00:00:00Z","author":[{"orcid":"0000-0003-2361-3953","full_name":"Paixao, Tiago","last_name":"Paixao","id":"2C5658E6-F248-11E8-B48F-1D18A9856A87","first_name":"Tiago"},{"full_name":"Barton, Nicholas H","last_name":"Barton","first_name":"Nicholas H","id":"4880FE40-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-8548-5240"}],"project":[{"grant_number":"250152","name":"Limits to selection in biology and in evolutionary computation","_id":"25B07788-B435-11E9-9278-68D0E5697425","call_identifier":"FP7"}],"status":"public","ec_funded":1,"type":"conference","day":"01","year":"2013","oa_version":"None","publication_status":"published","citation":{"mla":"Paixao, Tiago, and Nicholas H. Barton. “A Variance Decomposition Approach to the Analysis of Genetic Algorithms.” <i>Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation</i>, ACM, 2013, pp. 845–52, doi:<a href=\"https://doi.org/10.1145/2463372.2463470\">10.1145/2463372.2463470</a>.","ista":"Paixao T, Barton NH. 2013. A variance decomposition approach to the analysis of genetic algorithms. Proceedings of the 15th annual conference on Genetic and evolutionary computation. GECCO: Genetic and evolutionary computation conference, 845–852.","short":"T. Paixao, N.H. Barton, in:, Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation, ACM, 2013, pp. 845–852.","apa":"Paixao, T., &#38; Barton, N. H. (2013). A variance decomposition approach to the analysis of genetic algorithms. In <i>Proceedings of the 15th annual conference on Genetic and evolutionary computation</i> (pp. 845–852). Amsterdam, Netherlands: ACM. <a href=\"https://doi.org/10.1145/2463372.2463470\">https://doi.org/10.1145/2463372.2463470</a>","ieee":"T. Paixao and N. H. Barton, “A variance decomposition approach to the analysis of genetic algorithms,” in <i>Proceedings of the 15th annual conference on Genetic and evolutionary computation</i>, Amsterdam, Netherlands, 2013, pp. 845–852.","ama":"Paixao T, Barton NH. A variance decomposition approach to the analysis of genetic algorithms. In: <i>Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation</i>. ACM; 2013:845-852. doi:<a href=\"https://doi.org/10.1145/2463372.2463470\">10.1145/2463372.2463470</a>","chicago":"Paixao, Tiago, and Nicholas H Barton. “A Variance Decomposition Approach to the Analysis of Genetic Algorithms.” In <i>Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation</i>, 845–52. ACM, 2013. <a href=\"https://doi.org/10.1145/2463372.2463470\">https://doi.org/10.1145/2463372.2463470</a>."},"conference":{"start_date":"2013-07-06","name":"GECCO: Genetic and evolutionary computation conference","end_date":"2013-07-10","location":"Amsterdam, Netherlands"},"scopus_import":1,"doi":"10.1145/2463372.2463470","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"ACM","publist_id":"4173","abstract":[{"text":"Prediction of the evolutionary process is a long standing problem both in the theory of evolutionary biology and evolutionary computation (EC). It has long been realized that heritable variation is crucial to both the response to selection and the success of genetic algorithms. However, not all variation contributes in the same way to the response. Quantitative genetics has developed a large body of work trying to estimate and understand how different components of the variance in fitness in the population contribute to the response to selection. We illustrate how to apply some concepts of quantitative genetics to the analysis of genetic algorithms. In particular, we derive estimates for the short term prediction of the response to selection and we use variance decomposition to gain insight on local aspects of the landscape. Finally, we propose a new population based genetic algorithm that uses these methods to improve its operation.","lang":"eng"}],"language":[{"iso":"eng"}]}