Crossover and bloat in the functionality model of enzyme genetic programming

Lones, Michael A.; Tyrrell, Andy M.

doi:10.1109/cec.2002.1007059

Cited by 12 publications

(3 citation statements)

References 8 publications

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“…For both transfer and remove operations, the number of components targeted is chosen randomly within an upper limit (t u ). The benefits of this form of crossover over uniform and tree-based crossovers is discussed in Lones and Tyrrell (2002b). Mutation targets both functionalities (m f ) and activities (m a ).…”

Section: Evolving Program Representationsmentioning

confidence: 99%

Modelling biological evolvability: implicit context and variation filtering in enzyme genetic programming

Lones

Tyrrell

2004

Biosystems

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Section: Evolving Program Representationsmentioning

confidence: 99%

Modelling biological evolvability: implicit context and variation filtering in enzyme genetic programming

Lones

Tyrrell

2004

Biosystems

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“…Note that with Tantrix-GA's standard (indirect) coding and no slack, there are many flat plateaus, indicating neutral local landscapes. Provided that these plateaus are not too large, they can be advantageous for evolutionary search [12,19,14,15]. With the addition of more slack, the plateaus shrink.…”

Section: A More Direct Representationmentioning

confidence: 99%

Tantrix: A Minute to Learn, 100 (Genetic Algorithm) Generations to Master

Downing

2005

Genet Program Evolvable Mach

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The game of Tantrix™provides a challenging, mathematical and graphic domain for evolutionary computation. The simple task of forming long loops of colored arcs quickly becomes a search nightmare for humans and computers alike as the number of game pieces scales linearly. This paper introduces Tantrix-GA, a genetic algorithm that solves several types and sizes of Tantrix puzzles but still falls well short of (at least a few) human Tantrix experts. By introducing this problem to evolutionary computation researchers, we hope to motivate an evolutionary attack on the holy-grail Tantrix puzzles, one of which has yet to be solved by any intelligence, real or artificial.

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“…Bloating is an inherent GP problem and represents the accelerated growth of the trees in successive generations (Langdon and Poli, 1998;Lones and Tyrrell, 2002). This problem also relates to the execution time problem, because larger programs consume more resources.…”

mentioning

confidence: 99%

Automatic Generation of Cognitive Theories using Genetic Programming

Frı́as-Martı́nez

Gobet

2007

Minds & Machines

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Abstract.Cognitive neuroscience is the branch of neuroscience that studies the neural mechanisms underpinning cognition and develops theories explaining them. Within cognitive neuroscience, computational neuroscience focuses on modeling behavior, using theories expressed as computer programs. Up to now, computational theories have been formulated by neuroscientists. In this paper, we present a new approach to theory development in neuroscience: the automatic generation and testing of cognitive theories using genetic programming. Our approach evolves from experimental data cognitive theories that explain "the mental program" that subjects use to solve a specific task. As an example, we have focused on a typical neuroscience experiment, the delayed-match-to-sample (DMTS) task. The main goal of our approach is to develop a tool that neuroscientists can use to develop better cognitive theories.

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Crossover and bloat in the functionality model of enzyme genetic programming

Abstract: Abstract-The functionality model is a new approach in enzyme genetic programming which enables the evolution of variable length solutions whilst preserving local context. This paper introduces the model and presents an analysis of crossover and the evolution of program size.

Cited by 12 publications

References 8 publications

Modelling biological evolvability: implicit context and variation filtering in enzyme genetic programming

Modelling biological evolvability: implicit context and variation filtering in enzyme genetic programming

Tantrix: A Minute to Learn, 100 (Genetic Algorithm) Generations to Master

Automatic Generation of Cognitive Theories using Genetic Programming

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