Evolving Efficient Solutions to Complex Problems Using the Artificial Epigenetic Network

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

@InProceedings{turner2015evolving,

• author = "Alexander P. Turner and Martin A. Trefzer and Michael A. Lones and Andy M. Tyrrell",
• title = "Evolving Efficient Solutions to Complex Problems Using the Artificial Epigenetic Network",
• booktitle = "10th International Conference on Information Processing in Cells and Tissues, IPCAT 2015",
• year = "2015",
• editor = "Michael Lones and Andy Tyrrell and Stephen Smith and Gary Fogel",
• volume = "9303",
• series = "LNCS",
• address = "San Diego, CA, USA",
• month = sep # " 14-16",
• publisher = "Springer International Publishing",
• pages = "153--165",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1007/978-3-319-23108-2_13",
• abstract = "The artificial epigenetic network (AEN) is a computational model which is able to topologically modify its structure according to environmental stimulus. This approach is inspired by the functionality of epigenetics in nature, specifically, processes such as chromatin modifications which are able to dynamically modify the topology of gene regulatory networks. The AEN has previously been shown to perform well when applied to tasks which require a range of dynamical behaviours to be solved optimally. In addition, it has been shown that pruning of the AEN to remove non-functional elements can result in highly compact solutions to complex dynamical tasks. In this work, a method has been developed which provides the AEN with the ability to self prune throughout the optimisation process, whilst maintaining functionality. To test this hypothesis, the AEN is applied to a range of dynamical tasks and the most optimal solutions are analysed in terms of function and structure.",
• notes = "Affiliated with Department of Electronics, University of York",

}

Genetic Programming entries for Alexander P Turner Martin A Trefzer Michael A Lones Andrew M Tyrrell

Citations