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We present an investigation into the genotype-phenotype map in Position Independent Grammatical Evolution (πGE). Previous studies have shown πGE to exhibit a performance increase over standard Grammatical Evolution (GE). The only difference between the two approaches is in how the genotype-phenotype mapping process is performed. GE uses a leftmost non terminal expansion, while πGE evolves the order...
A key indicator of problem difficulty in evolutionary computation problems is the landscape's locality, that is whether the genotype-phenotype mapping preserves neighbourhood. In genetic programming the genotype and phenotype are not distinct, but the locality of the genotype-fitness mapping is of interest. In this paper we extend the original standard quantitative definition of locality to cover...
Locality - how well neighbouring genotypes correspond to neighbouring phenotypes - has been defined as a key element in Evolutionary Computation systems to explore and exploit the search space. Locality has been studied empirically using the typical Genetic Algorithms (GAs) representation (i.e., bitstrings),and it has been argued that locality plays an important role in the performance of evolution...
This paper studies the difference between Persistent Random Constants (PRC) and digit concatenation as methods for generating constants. It has been shown that certain problems have different fitness landscapes depending on how they are represented, independent of changes to the combinatorial search space, thus changing problem difficulty. In this case we show that the method for generating the constants...
This paper describes and tests the utility of a meta grammar approach to grammatical evolution (GE). Rather than employing a fixed grammar as is the case with canonical GE, under a meta grammar approach the grammar that is used to specify the construction of a syntactically correct solution is itself allowed to evolve. The ability to evolve a grammar in the context of GE means that useful bias towards...
We investigate the usefulness of a subtree deactivation control mechanism which is open to evolutionary learning. It is hypothesised that this representation confers an adaptive advantage in dynamic environments over the standard sub-tree representation adopted in Genetic Programming. Results presented on benchmark dynamic problem instances provides evidence to support that such an adaptive advantage...
Following earlier claims that Quantum-inspired Evolutionary Algorithm (QIEA) may offer advantages in high dimensional environments, this paper tests a real-valued QIEA on a series of benchmark functions of varying dimensionality in order to examine its scalability. The results are compared with those from a genetic algorithm using both a binary and real-valued representation. The results show that...
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