Alignment-based genetic programming for real life applications

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

@Article{Vanneschi:2019:swarmEC,

• author = "Leonardo Vanneschi and Mauro Castelli and Kristen Scott and Leonardo Trujillo",
• title = "Alignment-based genetic programming for real life applications",
• journal = "Swarm and Evolutionary Computation",
• year = "2019",
• volume = "44",
• pages = "840--851",
• month = feb,
• keywords = "genetic algorithms, genetic programming, Geometric semantic operators, Alignment, Error space, Real-life applications",
• ISSN = "2210-6502",
• URL = "http://www.sciencedirect.com/science/article/pii/S2210650218300208",
• DOI = "doi:10.1016/j.swevo.2018.09.006",
• size = "12 pages",
• abstract = "A recent discovery has attracted the attention of many researchers in the field of genetic programming: given individuals with particular characteristics of alignment in the error space, called optimally aligned, it is possible to reconstruct a globally optimal solution. Furthermore, recent preliminary experiments have shown that an indirect search consisting of looking for optimally aligned individuals can have benefits in terms of generalization ability compared to a direct search for optimal solutions. For this reason, defining genetic programming systems that look for optimally aligned individuals is becoming an ambitious and important objective. Nevertheless, the systems that have been introduced so far present important limitations that make them unusable in practice, particularly for complex real-life applications. In this paper, we overcome those limitations, and we present the first usable alignment-based genetic programming system, called nested alignment genetic programming (NAGP). The presented...",
• notes = "also known as \cite{VANNESCHI2018}",

}

Genetic Programming entries for Leonardo Vanneschi Mauro Castelli Kristen M Scott Leonardo Trujillo

Citations