Multi-Representation Genetic Programming: A Case Study on Tree-based and Linear Representations

• URL: http://arxiv.org/abs/2405.14268v1
• Date: Thu, 23 May 2024 07:47:06 GMT
• Title: Multi-Representation Genetic Programming: A Case Study on Tree-based and Linear Representations
• Abstract summary: This paper proposes a multi-representation GP algorithm based on tree-based and linear representations. We also develop a new cross-representation crossover operator to harness the interplay between tree-based and linear representations. Empirical results show that navigating the learned knowledge between basic tree-based and linear representations successfully improves the effectiveness of GP.
• Score: 4.999866990999739
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing genetic programming (GP) methods are typically designed based on a certain representation, such as tree-based or linear representations. These representations show various pros and cons in different domains. However, due to the complicated relationships among representation and fitness landscapes of GP, it is hard to intuitively determine which GP representation is the most suitable for solving a certain problem. Evolving programs (or models) with multiple representations simultaneously can alternatively search on different fitness landscapes since representations are highly related to the search space that essentially defines the fitness landscape. Fully using the latent synergies among different GP individual representations might be helpful for GP to search for better solutions. However, existing GP literature rarely investigates the simultaneous effective use of evolving multiple representations. To fill this gap, this paper proposes a multi-representation GP algorithm based on tree-based and linear representations, which are two commonly used GP representations. In addition, we develop a new cross-representation crossover operator to harness the interplay between tree-based and linear representations. Empirical results show that navigating the learned knowledge between basic tree-based and linear representations successfully improves the effectiveness of GP with solely tree-based or linear representation in solving symbolic regression and dynamic job shop scheduling problems.

Related papers

• Minimalist Genetic Programming [0.456877715768796]
  Genetic programming (GP) is based on two important insights.<n>This paper presents Minimalist Genetic Programming (MGP), an algorithm that like GP is biologically inspired.<n>MGP takes inspiration from the Minimalist Program to human language, in which syntax is understood as an optimal solution to the problem of linking two other mental systems.
  arXiv  Detail & Related papers  (2026-06-08T22:51:58Z)
• OFA-MAS: One-for-All Multi-Agent System Topology Design based on Mixture-of-Experts Graph Generative Models [57.94189874119267]
  Multi-Agent Systems (MAS) offer a powerful paradigm for solving complex problems.<n>Current graph learning-based design methodologies often adhere to a "one-for-one" paradigm.<n>We propose OFA-TAD, a one-for-all framework that generates adaptive collaboration graphs for any task described in natural language.
  arXiv  Detail & Related papers  (2026-01-19T12:23:44Z)
• Decoupling Representation and Learning in Genetic Programming: the LaSER Approach [0.0]
  We propose LaSER -- a framework that decouples representation evolution from lifetime learning.<n>We show for the first time that LaSER can outcompete standard GP and GP followed by linear regression.<n>By separating the roles of representation and adaptation, LaSER offers a principled and framework for symbolic regression and classification.
  arXiv  Detail & Related papers  (2025-05-22T21:59:38Z)
• TinyverseGP: Towards a Modular Cross-domain Benchmarking Framework for Genetic Programming [2.764155611699866]
  Genetic programming (GP) is capable of tackling multiple problem domains. We propose a unified framework, dubbed TinyverseGP, which provides support to multiple representations and problem domains.
  arXiv  Detail & Related papers  (2025-04-14T14:14:27Z)
• From Primes to Paths: Enabling Fast Multi-Relational Graph Analysis [5.008498268411793]
  Multi-relational networks capture intricate relationships in data and have diverse applications across fields such as biomedical, financial, and social sciences. This work extends the Prime Adjacency Matrices framework, which employs prime numbers to represent distinct relations within a network uniquely.
  arXiv  Detail & Related papers  (2024-11-17T18:43:01Z)
• A Functional Analysis Approach to Symbolic Regression [0.990319860068191]
  Symbolic regression (SR) poses a significant challenge for randomized searchs. Traditional genetic programming (GP) algorithms exhibit limited performance when tree-based representations are used for SR. We introduce a novel SR approach that draws insights from functional analysis.
  arXiv  Detail & Related papers  (2024-02-09T10:24:47Z)
• ViTree: Single-path Neural Tree for Step-wise Interpretable Fine-grained Visual Categorization [56.37520969273242]
  We introduce ViTree, a novel approach for fine-grained visual categorization. By traversing the tree paths, ViTree effectively selects patches from transformer-processed features to highlight informative local regions. This patch and path selectivity enhances model interpretability of ViTree, enabling better insights into the model's inner workings.
  arXiv  Detail & Related papers  (2024-01-30T14:32:25Z)
• Interactive Segmentation as Gaussian Process Classification [58.44673380545409]
  Click-based interactive segmentation (IS) aims to extract the target objects under user interaction. Most of the current deep learning (DL)-based methods mainly follow the general pipelines of semantic segmentation. We propose to formulate the IS task as a Gaussian process (GP)-based pixel-wise binary classification model on each image.
  arXiv  Detail & Related papers  (2023-02-28T14:01:01Z)
• A Multi-scale Graph Signature for Persistence Diagrams based on Return Probabilities of Random Walks [1.745838188269503]
  We explore the use of a family of multi-scale graph signatures to enhance the robustness of topological features. We propose a deep learning architecture to handle this set input. Experiments on benchmark graph classification datasets demonstrate that our proposed architecture outperforms other persistent homology-based methods.
  arXiv  Detail & Related papers  (2022-09-28T17:30:27Z)
• On genetic programming representations and fitness functions for interpretable dimensionality reduction [0.0]
  Dimensionality reduction (DR) is an important technique for data exploration and knowledge discovery. Most of the main DR methods are either linear (e.g., PCA) or do not provide an explicit mapping between the original data and its lower-dimensional representation. Recently, genetic programming (GP) has been used to evolve interpretable DR mappings in the form of symbolic expressions.
  arXiv  Detail & Related papers  (2022-03-01T15:12:17Z)
• Dist2Cycle: A Simplicial Neural Network for Homology Localization [66.15805004725809]
  Simplicial complexes can be viewed as high dimensional generalizations of graphs that explicitly encode multi-way ordered relations. We propose a graph convolutional model for learning functions parametrized by the $k$-homological features of simplicial complexes.
  arXiv  Detail & Related papers  (2021-10-28T14:59:41Z)
• Spatial-spectral Hyperspectral Image Classification via Multiple Random Anchor Graphs Ensemble Learning [88.60285937702304]
  This paper proposes a novel spatial-spectral HSI classification method via multiple random anchor graphs ensemble learning (RAGE) Firstly, the local binary pattern is adopted to extract the more descriptive features on each selected band, which preserves local structures and subtle changes of a region. Secondly, the adaptive neighbors assignment is introduced in the construction of anchor graph, to reduce the computational complexity.
  arXiv  Detail & Related papers  (2021-03-25T09:31:41Z)
• Joint Network Topology Inference via Structured Fusion Regularization [70.30364652829164]
  Joint network topology inference represents a canonical problem of learning multiple graph Laplacian matrices from heterogeneous graph signals. We propose a general graph estimator based on a novel structured fusion regularization. We show that the proposed graph estimator enjoys both high computational efficiency and rigorous theoretical guarantee.
  arXiv  Detail & Related papers  (2021-03-05T04:42:32Z)
• Learning the Implicit Semantic Representation on Graph-Structured Data [57.670106959061634]
  Existing representation learning methods in graph convolutional networks are mainly designed by describing the neighborhood of each node as a perceptual whole. We propose a Semantic Graph Convolutional Networks (SGCN) that explores the implicit semantics by learning latent semantic-paths in graphs.
  arXiv  Detail & Related papers  (2021-01-16T16:18:43Z)
• Graph-based State Representation for Deep Reinforcement Learning [1.5901689240516976]
  We exploit the fact that the underlying Markov decision process (MDP) represents a graph, which enables us to incorporate the topological information for effective state representation learning. Motivated by the recent success of node representations for several graph analytical tasks we specifically investigate the capability of node representation learning methods to effectively encode the topology of the underlying MDP in Deep RL. We find that all embedding methods outperform the commonly used matrix representation of grid-world environments in all of the studied cases.
  arXiv  Detail & Related papers  (2020-04-29T05:43:15Z)

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.