Enzyme Genetic Programming: Modelling Biological Evolvability in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@PhdThesis{lones:thesis,

• author = "Michael A. Lones",
• title = "Enzyme Genetic Programming: Modelling Biological Evolvability in Genetic Programming",
• school = "The University of York",
• year = "2003",
• address = "Heslington, York, YO10 5DD, UK",
• month = sep,
• keywords = "genetic algorithms, genetic programming, evolvability, representation, self-organisation, biological modelling",
• URL = "http://www.macs.hw.ac.uk/~ml355/common/thesis/main.html",
• URL = "http://www-users.york.ac.uk/~mal503/common/thesis/michael_lones_thesis.pdf",
• URL = "http://ethos.bl.uk/OrderDetails.do?did=8&uin=uk.bl.ethos.399653",
• size = "200 pages",
• abstract = "This thesis introduces a new approach to program representation in genetic programming in which interactions between program components are expressed in terms of a component's behaviour rather through its relative position within a representation or through other non-behavioural systems of reference. This approach has the advantage that a component's behaviour is expressed in a way that is independent of any particular program it finds itself within; and thereby overcomes the problem when using conventional program representations whereby program components lose their behavioural context following recombination. More generally, this implicit context representation leads to a process of meaningful variation filtering; whereby inappropriate change induced by variation operators can be wholly or partially ignored. This occurs as a consequence of program behaviours emerging from the self-organisation of program components, ignoring those components which do not fit the contexts declared by the other components within the program. This process results in gradual change within the behaviour of a program during evolution. This thesis also presents results which show that implicit context representation leads to better size evolution characteristics than conventional genetic programming; and that functional redundancy and Lamarckian reinforcement learning both improve evolutionary search, agreeing with previous research by other authors.",
• notes = "small section on 'homologous crossovers' Gone sep 2023 http://folk.ntnu.no/lones/thesis/c7.html#tth_sEc7.2 These choose crossover points non-randomly according to recognition of genetic homology (bits that look the same). uk.bl.ethos.399653",

}

Genetic Programming entries for Michael A Lones

Citations