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Genetic Programming and Autoconstructive Evolution with the Push Programming Language

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Push is a programming language designed for the expression of evolving programs within an evolutionary computation system. This article describes Push and illustrates some of the opportunities that it presents for evolutionary computation. Two evolutionary computation systems, PushGP and Pushpop, are described in detail. PushGP is a genetic programming system that evolves Push programs to solve computational problems. Pushpop, an autoconstructive evolution system, also evolves Push programs but does so while simultaneously evolving its own evolutionary mechanisms.
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... While there exist approaches towards more directed generation of offspring (e.g. building probabilistic models of high-performing programs [25], evolving reproduction operators [26], or applying less-impactful mutation operators [24]), the problem remains at core unsolved. ...
... For Tiny GP, The mutation rate was tuned by hand for each problem. While many more sophisticated mutation operators in GP exist [25,26], the motivation for these experiments is mainly to highlight the potential for LLM-based directed mutation operators to make sophisticated movements along the manifold of code. ...
... CPPN-Fixed does not allow the core functionality of the CPPN encoding (encapsulated in the query cppn function) to change, whereas CPPN-Mutable includes the source code for that function, thereby enabling the CPPN encoding itself also to evolve. wc.add_muscle(sides [3], sides[0]) 24 25 # one prong of the square is a distance muscle 26 wc.add_muscle(sides [3], center) 27 28 # the other prongs from the center of the square are active ...
Preprint
This paper pursues the insight that large language models (LLMs) trained to generate code can vastly improve the effectiveness of mutation operators applied to programs in genetic programming (GP). Because such LLMs benefit from training data that includes sequential changes and modifications, they can approximate likely changes that humans would make. To highlight the breadth of implications of such evolution through large models (ELM), in the main experiment ELM combined with MAP-Elites generates hundreds of thousands of functional examples of Python programs that output working ambulating robots in the Sodarace domain, which the original LLM had never seen in pre-training. These examples then help to bootstrap training a new conditional language model that can output the right walker for a particular terrain. The ability to bootstrap new models that can output appropriate artifacts for a given context in a domain where zero training data was previously available carries implications for open-endedness, deep learning, and reinforcement learning. These implications are explored here in depth in the hope of inspiring new directions of research now opened up by ELM.
... PushGP evolves programs in the language Push, a stack-based programming language built specifically for use in genetic programming [62,63]. Every data type has its own stack, and each Push instruction acts by pushing and popping various Table 3 Instructions and data types used in our PushGP implementation of each problem. ...
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Thesis
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