Genetic algorithms with implicit memory

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dc.contributor.author Morris, Robert
dc.date.accessioned 2011-09-01T16:15:52Z
dc.date.available 2011-09-01T16:15:52Z
dc.date.issued 2011
dc.identifier.uri http://hdl.handle.net/2086/5186
dc.description.abstract This thesis investigates the workings of genetic algorithms in dynamic optimisation problems where fitness landscapes materialise that are identical to, or resemble in some way, landscapes previously encountered. The objective is to appraise the performances of the various approaches offered by the GAs. Approaches specifically tailored for different kinds of dynamic environment lie outside the remit of the thesis. The main topics that are explored are: genetic redundancy, modularity, neutral evolution, explicit memory, and implicit memory. It is in the matter of implicit memory that the thesis makes the majority of its novel contributions. It is demonstrated via experimental analysis that the pre-existing techniques are deficient, and a new algorithm – the pointer genetic algorithm (pGA) – is expounded and assessed in an attempt to offer an improvement. It is shown that though it outperforms its rivals, it cannot attain the performance levels of an explicit memory algorithm (that is, an algorithm using an external memory bank). The main claims of the thesis are that with regard to memory, the pre-existing implicit-memory algorithms are deficient, the new pointer GA is superior, and that because all of the implicit approaches are inferior to explicit approaches, it is explicit approaches that should be used in real-world problem solving. en
dc.language.iso en en
dc.publisher De Montfort University en
dc.title Genetic algorithms with implicit memory en
dc.type Thesis or dissertation en
dc.publisher.department Faculty of Technology en
dc.type.qualificationlevel Masters en
dc.type.qualificationname MPhil en


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