SignalGP-Lite: Event Driven Genetic Programming Library for Large-Scale Artificial Life Applications

• URL: http://arxiv.org/abs/2108.00382v1
• Date: Sun, 1 Aug 2021 07:20:49 GMT
• Title: SignalGP-Lite: Event Driven Genetic Programming Library for Large-Scale Artificial Life Applications
• Authors: Matthew Andres Moreno, Santiago Rodriguez Papa, Alexander Lalejini, Charles Ofria
• Abstract summary: Event-driven genetic programming representations have been shown to outperform traditional imperative representations on interaction-intensive problems. Event-driven approach organizes genome content into modules that are triggered in response to environmental signals. SignalGP library caters to traditional program synthesis applications.
• Score: 62.997667081978825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Event-driven genetic programming representations have been shown to outperform traditional imperative representations on interaction-intensive problems. The event-driven approach organizes genome content into modules that are triggered in response to environmental signals, simplifying simulation design and implementation. Existing work developing event-driven genetic programming methodology has largely used the SignalGP library, which caters to traditional program synthesis applications. The SignalGP-Lite library enables larger-scale artificial life experiments with streamlined agents by reducing control flow overhead and trading run-time flexibility for better performance due to compile-time configuration. Here, we report benchmarking experiments that show an 8x to 30x speedup. We also report solution quality equivalent to SignalGP on two benchmark problems originally developed to test the ability of evolved programs to respond to a large number of signals and to modulate signal response based on context.

Related papers

• Exploring RL-based LLM Training for Formal Language Tasks with Programmed Rewards [49.7719149179179]
  This paper investigates the feasibility of using PPO for reinforcement learning (RL) from explicitly programmed reward signals. We focus on tasks expressed through formal languages, such as programming, where explicit reward functions can be programmed to automatically assess quality of generated outputs. Our results show that pure RL-based training for the two formal language tasks is challenging, with success being limited even for the simple arithmetic task.
  arXiv  Detail & Related papers  (2024-10-22T15:59:58Z)
• MLXP: A Framework for Conducting Replicable Experiments in Python [63.37350735954699]
  We propose MLXP, an open-source, simple, and lightweight experiment management tool based on Python. It streamlines the experimental process with minimal overhead while ensuring a high level of practitioner overhead.
  arXiv  Detail & Related papers  (2024-02-21T14:22:20Z)
• Entropy-Regularized Token-Level Policy Optimization for Language Agent Reinforcement [67.1393112206885]
  Large Language Models (LLMs) have shown promise as intelligent agents in interactive decision-making tasks. We introduce Entropy-Regularized Token-level Policy Optimization (ETPO), an entropy-augmented RL method tailored for optimizing LLMs at the token level. We assess the effectiveness of ETPO within a simulated environment that models data science code generation as a series of multi-step interactive tasks.
  arXiv  Detail & Related papers  (2024-02-09T07:45:26Z)
• SymbolNet: Neural Symbolic Regression with Adaptive Dynamic Pruning [1.0356366043809717]
  We propose a neural network approach to symbolic regression in a novel framework that allows dynamic pruning of model weights, input features, and mathematical operators in a single training process. Our approach enables symbolic regression to achieve fast inference with nanosecond-scale latency on FPGAs for high-dimensional datasets in environments with stringent computational resource constraints.
  arXiv  Detail & Related papers  (2024-01-18T12:51:38Z)
• Racing Control Variable Genetic Programming for Symbolic Regression [22.101494818629963]
  Symbolic regression is one of the most crucial tasks in AI for science. We propose Racing Control Variable Genetic Programming (Racing-CVGP), which carries out multiple experiment schedules simultaneously. A selection scheme similar to that used in selecting good symbolic equations in the genetic programming process is implemented to ensure that promising experiment schedules eventually win over the average ones.
  arXiv  Detail & Related papers  (2023-09-13T21:38:06Z)
• Transformer-based Planning for Symbolic Regression [18.90700817248397]
  We propose TPSR, a Transformer-based Planning strategy for Symbolic Regression. Unlike conventional decoding strategies, TPSR enables the integration of non-differentiable feedback, such as fitting accuracy and complexity. Our approach outperforms state-of-the-art methods, enhancing the model's fitting-complexity trade-off, Symbolic abilities, and robustness to noise.
  arXiv  Detail & Related papers  (2023-03-13T03:29:58Z)
• Near-optimal Policy Identification in Active Reinforcement Learning [84.27592560211909]
  AE-LSVI is a novel variant of the kernelized least-squares value RL (LSVI) algorithm that combines optimism with pessimism for active exploration. We show that AE-LSVI outperforms other algorithms in a variety of environments when robustness to the initial state is required.
  arXiv  Detail & Related papers  (2022-12-19T14:46:57Z)
• CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning [92.36705236706678]
  "CodeRL" is a new framework for program synthesis tasks through pretrained LMs and deep reinforcement learning. During inference, we introduce a new generation procedure with a critical sampling strategy. For the model backbones, we extended the encoder-decoder architecture of CodeT5 with enhanced learning objectives.
  arXiv  Detail & Related papers  (2022-07-05T02:42:15Z)
• A Graph Deep Learning Framework for High-Level Synthesis Design Space Exploration [11.154086943903696]
  High-Level Synthesis is a solution for fast prototyping application-specific hardware. We propose HLS, for the first time in the literature, graph neural networks that jointly predict acceleration performance and hardware costs. We show that our approach achieves prediction accuracy comparable with that of commonly used simulators.
  arXiv  Detail & Related papers  (2021-11-29T18:17:45Z)
• Mining Environment Assumptions for Cyber-Physical System Models [0.19336815376402716]
  We show that a subset of input signals for which the corresponding output signals satisfy the output requirement can be compactly described. We propose an algorithm to mine such an environment assumption using a supervised learning technique. We demonstrate experimental results on real world data from several domains including transportation and health care.
  arXiv  Detail & Related papers  (2020-05-18T03:05:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.