Michael O'Neill

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We describe a Genetic Algorithm that can evolve complete programs. Using a variable length linear genome to govern how a Backus Naur Form grammar deenition is mapped to a program, expressions and programs of arbitrary complexity may be evolved. Other automatic programming methods are described, before our system, Grammatical Evolution, is applied to a(More)
Grammar formalisms are one of the key representation structures in Computer Science. So it is not surprising that they have also become important as a method for formalizing constraints in Genetic Programming (GP). Practical grammar-based GP systems first appeared in the mid 1990s, and have subsequently become an important strand in GP research and(More)
We investigate the effects of semantically-based crossover operators in genetic programming, applied to real-valued symbolic regression problems. We propose two new relations derived from the semantic distance between subtrees, known as semantic equivalence and semantic similarity. These relations are used to guide variants of the crossover operator,(More)
This study examines the possibility of evolving the grammar that Grammatical Evolution uses to specify the construction of a syntactically correct solution. As the grammar dictates the space of symbols that can be used in a solution, its evolution represents the evolution of the genetic code itself. Results provide evidence to show that the coevolution of(More)
Parkinson's disease is a neurodegenerative movement disorder that is characterized by a loss of nigrostriatal dopamine-containing neurons. Unexpectedly, there is a reduced incidence of Parkinson's disease in tobacco users. This finding is important because the identification of the component(s) responsible for this effect could lead to therapeutic(More)
This paper presents the use of design grammars to evolve playable 2D platform levels through grammatical evolution (GE). Representing levels using design grammars allows simple encoding of important level design constraints, and allows remarkably compact descriptions of large spaces of levels. The expressive range of the GE-based level generator is analyzed(More)
It is approximately 50 years since the first computational experiments were conducted in what has become known today as the field of Genetic Programming (GP), twenty years since John Koza named and popularised the method, and ten years since the first issue appeared of the Genetic Programming & Evolvable Machines journal. In particular, during the past two(More)
A macroergonomics intervention consisting of flexible workspace design and ergonomics training was conducted to examine the effects on psychosocial work environment, musculoskeletal health, and work effectiveness in a computer-based office setting. Knowledge workers were assigned to one of four conditions: flexible workspace (n=121), ergonomics training(More)