CGP++ : A Modern C++ Implementation of Cartesian Genetic Programming
- URL: http://arxiv.org/abs/2406.09038v1
- Date: Thu, 13 Jun 2024 12:22:08 GMT
- Title: CGP++ : A Modern C++ Implementation of Cartesian Genetic Programming
- Authors: Roman Kalkreuth, Thomas Baeck,
- Abstract summary: The reference implementation of Cartesian Genetic Programming (CGP) was written in the C programming language.
We propose the first modern C++ implementation of CGP that pursues object-oriented design and generic programming paradigm.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The reference implementation of Cartesian Genetic Programming (CGP) was written in the C programming language. C inherently follows a procedural programming paradigm, which entails challenges in providing a reusable and scalable implementation model for complex structures and methods. Moreover, due to the limiting factors of C, the reference implementation of CGP does not provide a generic framework and is therefore restricted to a set of predefined evaluation types. Besides the reference implementation, we also observe that other existing implementations are limited with respect to the features provided. In this work, we therefore propose the first version of a modern C++ implementation of CGP that pursues object-oriented design and generic programming paradigm to provide an efficient implementation model that can facilitate the discovery of new problem domains and the implementation of complex advanced methods that have been proposed for CGP over time. With the proposal of our new implementation, we aim to generally promote interpretability, accessibility and reproducibility in the field of CGP.
Related papers
- Generative Actor Critic [74.04971271003869]
Generative Actor Critic (GAC) is a novel framework that decouples sequential decision-making by reframing textitpolicy evaluation as learning a generative model of the joint distribution over trajectories and returns.<n>Experiments on Gym-MuJoCo and Maze2D benchmarks demonstrate GAC's strong offline performance and significantly enhanced offline-to-online improvement compared to state-of-the-art methods.
arXiv Detail & Related papers (2025-12-25T06:31:11Z) - Partial Transportability for Domain Generalization [56.37032680901525]
Building on the theory of partial identification and transportability, this paper introduces new results for bounding the value of a functional of the target distribution.
Our contribution is to provide the first general estimation technique for transportability problems.
We propose a gradient-based optimization scheme for making scalable inferences in practice.
arXiv Detail & Related papers (2025-03-30T22:06:37Z) - Chain of Grounded Objectives: Bridging Process and Goal-oriented Prompting for Code Generation [21.084058098777803]
Chain of Grounded Objectives (CGO) is a method that embeds functional objectives into input prompts to enhance code generation.
By leveraging appropriately structured objectives as input and avoiding explicit sequential procedures, CGO adapts effectively to the structured nature of programming tasks.
arXiv Detail & Related papers (2025-01-23T01:45:09Z) - Last-Iterate Global Convergence of Policy Gradients for Constrained Reinforcement Learning [62.81324245896717]
We introduce an exploration-agnostic algorithm, called C-PG, which exhibits global last-ite convergence guarantees under (weak) gradient domination assumptions.
We numerically validate our algorithms on constrained control problems, and compare them with state-of-the-art baselines.
arXiv Detail & Related papers (2024-07-15T14:54:57Z) - Novelty and Lifted Helpful Actions in Generalized Planning [14.513354207511151]
We introduce the notion of action novelty rank, which computes novelty with respect to a planning program.
We propose novelty-based generalized planning solvers, which prune a newly generated planning program if its most frequent action repetition is greater than a given bound $v$.
arXiv Detail & Related papers (2023-07-03T03:44:12Z) - Solving Novel Program Synthesis Problems with Genetic Programming using
Parametric Polymorphism [0.0]
We show that Code-building Genetic Programming (CBGP) compiles type-safe programs from linear genomes using stack-based compilation and a formal type system.
CBGP is able to solve problems with all of these properties, where every other GP system that we know of has restrictions that make it unable to even consider problems with these properties.
arXiv Detail & Related papers (2023-06-08T00:10:07Z) - Harmonizing Base and Novel Classes: A Class-Contrastive Approach for
Generalized Few-Shot Segmentation [78.74340676536441]
We propose a class contrastive loss and a class relationship loss to regulate prototype updates and encourage a large distance between prototypes.
Our proposed approach achieves new state-of-the-art performance for the generalized few-shot segmentation task on PASCAL VOC and MS COCO datasets.
arXiv Detail & Related papers (2023-03-24T00:30:25Z) - Autoregressive Structured Prediction with Language Models [73.11519625765301]
We describe an approach to model structures as sequences of actions in an autoregressive manner with PLMs.
Our approach achieves the new state-of-the-art on all the structured prediction tasks we looked at.
arXiv Detail & Related papers (2022-10-26T13:27:26Z) - A General Framework for Sample-Efficient Function Approximation in
Reinforcement Learning [132.45959478064736]
We propose a general framework that unifies model-based and model-free reinforcement learning.
We propose a novel estimation function with decomposable structural properties for optimization-based exploration.
Under our framework, a new sample-efficient algorithm namely OPtimization-based ExploRation with Approximation (OPERA) is proposed.
arXiv Detail & Related papers (2022-09-30T17:59:16Z) - Multi-Objective Policy Gradients with Topological Constraints [108.10241442630289]
We present a new algorithm for a policy gradient in TMDPs by a simple extension of the proximal policy optimization (PPO) algorithm.
We demonstrate this on a real-world multiple-objective navigation problem with an arbitrary ordering of objectives both in simulation and on a real robot.
arXiv Detail & Related papers (2022-09-15T07:22:58Z) - Representation and Synthesis of C++ Programs for Generalized Planning [2.752817022620644]
The paper introduces a novel representation for Generalized Planning (GP) problems, and their solutions, as C++ programs.
Our C++ representation allows to formally proving the termination of generalized plans, and to specifying their complexity w.r.t.
Characterizing the complexity of C++ generalized plans enables the application of a search that enumerates the space of possible GP solutions in order of complexity.
arXiv Detail & Related papers (2022-06-29T09:13:21Z) - Functional Code Building Genetic Programming [0.0]
Code Building Genetic Programming (CBGP) is a recently introduced GP method for general program synthesis.
We show that a functional programming language and a Hindley-Milner type system can be used to evolve type-safe programs.
arXiv Detail & Related papers (2022-06-09T15:22:33Z) - Revisiting GANs by Best-Response Constraint: Perspective, Methodology,
and Application [49.66088514485446]
Best-Response Constraint (BRC) is a general learning framework to explicitly formulate the potential dependency of the generator on the discriminator.
We show that even with different motivations and formulations, a variety of existing GANs ALL can be uniformly improved by our flexible BRC methodology.
arXiv Detail & Related papers (2022-05-20T12:42:41Z) - Code Building Genetic Programming [0.0]
We introduce Code Building Genetic Programming (CBGP) as a framework within which this can be done.
CBGP produces a computational graph that can be executed or translated into source code of a host language.
arXiv Detail & Related papers (2020-08-09T04:33:04Z)
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.