GARA: A novel approach to Improve Genetic Algorithms' Accuracy and Efficiency by Utilizing Relationships among Genes

• URL: http://arxiv.org/abs/2404.18955v1
• Date: Sun, 28 Apr 2024 08:33:39 GMT
• Title: GARA: A novel approach to Improve Genetic Algorithms' Accuracy and Efficiency by Utilizing Relationships among Genes
• Authors: Zhaoning Shi, Meng Xiang, Zhaoyang Hai, Xiabi Liu, Yan Pei,
• Abstract summary: We propose Gene Regulatory Genetic Algorithm (GRGA), which is the first time to utilize relationships among genes for improving GA's accuracy and efficiency. We use a directed multipartite graph encapsulating the solution space, called RGGR, where each node corresponds to a gene in the solution and the edge represents the relationship between adjacent nodes. The obtained RGGR is then employed to determine appropriate loci of crossover and mutation operators, thereby directing the evolutionary process toward faster and better convergence.
• Score: 1.7226572355808027
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Genetic algorithms have played an important role in engineering optimization. Traditional GAs treat each gene separately. However, biophysical studies of gene regulatory networks revealed direct associations between different genes. It inspires us to propose an improvement to GA in this paper, Gene Regulatory Genetic Algorithm (GRGA), which, to our best knowledge, is the first time to utilize relationships among genes for improving GA's accuracy and efficiency. We design a directed multipartite graph encapsulating the solution space, called RGGR, where each node corresponds to a gene in the solution and the edge represents the relationship between adjacent nodes. The edge's weight reflects the relationship degree and is updated based on the idea that the edges' weights in a complete chain as candidate solution with acceptable or unacceptable performance should be strengthened or reduced, respectively. The obtained RGGR is then employed to determine appropriate loci of crossover and mutation operators, thereby directing the evolutionary process toward faster and better convergence. We analyze and validate our proposed GRGA approach in a single-objective multimodal optimization problem, and further test it on three types of applications, including feature selection, text summarization, and dimensionality reduction. Results illustrate that our GARA is effective and promising.

Related papers

• Predicting Genetic Mutation from Whole Slide Images via Biomedical-Linguistic Knowledge Enhanced Multi-label Classification [119.13058298388101]
  We develop a Biological-knowledge enhanced PathGenomic multi-label Transformer to improve genetic mutation prediction performances. BPGT first establishes a novel gene encoder that constructs gene priors by two carefully designed modules. BPGT then designs a label decoder that finally performs genetic mutation prediction by two tailored modules.
  arXiv  Detail & Related papers  (2024-06-05T06:42:27Z)
• Genetic Engineering Algorithm (GEA): An Efficient Metaheuristic Algorithm for Solving Combinatorial Optimization Problems [1.8434042562191815]
  Genetic Algorithms (GAs) are known for their efficiency in solving optimization problems. This paper proposes a new metaheuristic algorithm called the Genetic Engineering Algorithm (GEA) that draws inspiration from genetic engineering concepts.
  arXiv  Detail & Related papers  (2023-09-28T13:05:30Z)
• Edge-set reduction to efficiently solve the graph partitioning problem with the genetic algorithm [0.0]
  We investigate the impact of modifying the size of the chromosomes on the edge based genetic algorithms (GA) We show that for big dense instances, the size of the encoding representation becomes too huge and affects GA's efficiency.
  arXiv  Detail & Related papers  (2023-07-19T18:39:15Z)
• The FAIRy Tale of Genetic Algorithms [1.0957528713294875]
  We have extended Findable, Accessible, Interoperable and Reusable (FAIR) data principles to enable Genetic and reusability of algorithms. We have presented an overview of methodological developments and variants of GA that makes it challenging to reproduce or even find the right source. This work can be extended to numerous machine learning algorithms/methods.
  arXiv  Detail & Related papers  (2023-04-29T11:36:09Z)
• Unsupervised ensemble-based phenotyping helps enhance the discoverability of genes related to heart morphology [57.25098075813054]
  We propose a new framework for gene discovery entitled Un Phenotype Ensembles. It builds a redundant yet highly expressive representation by pooling a set of phenotypes learned in an unsupervised manner. These phenotypes are then analyzed via (GWAS), retaining only highly confident and stable associations.
  arXiv  Detail & Related papers  (2023-01-07T18:36:44Z)
• Granger causal inference on DAGs identifies genomic loci regulating transcription [77.58911272503771]
  GrID-Net is a framework based on graph neural networks with lagged message passing for Granger causal inference on DAG-structured systems. Our application is the analysis of single-cell multimodal data to identify genomic loci that mediate the regulation of specific genes.
  arXiv  Detail & Related papers  (2022-10-18T21:15:10Z)
• Relation Embedding based Graph Neural Networks for Handling Heterogeneous Graph [58.99478502486377]
  We propose a simple yet efficient framework to make the homogeneous GNNs have adequate ability to handle heterogeneous graphs. Specifically, we propose Relation Embedding based Graph Neural Networks (RE-GNNs), which employ only one parameter per relation to embed the importance of edge type relations and self-loop connections.
  arXiv  Detail & Related papers  (2022-09-23T05:24:18Z)
• Direct Mutation and Crossover in Genetic Algorithms Applied to Reinforcement Learning Tasks [0.9137554315375919]
  This paper will focus on applying neuroevolution using a simple genetic algorithm (GA) to find the weights of a neural network that produce optimally behaving agents. We present two novel modifications that improve the data efficiency and speed of convergence when compared to the initial implementation.
  arXiv  Detail & Related papers  (2022-01-13T07:19:28Z)
• Result Diversification by Multi-objective Evolutionary Algorithms with Theoretical Guarantees [94.72461292387146]
  We propose to reformulate the result diversification problem as a bi-objective search problem, and solve it by a multi-objective evolutionary algorithm (EA) We theoretically prove that the GSEMO can achieve the optimal-time approximation ratio, $1/2$. When the objective function changes dynamically, the GSEMO can maintain this approximation ratio in running time, addressing the open question proposed by Borodin et al.
  arXiv  Detail & Related papers  (2021-10-18T14:00:22Z)
• Otimizacao de Redes Neurais atraves de Algoritmos Geneticos Celulares [0.0]
  The goal of this methodology is to find compact networks whit good performance for classification problems. The use of CGAs aims at seeking the components of the RNA in the same way that a common Genetic Algorithm (GA) The location imposed by the CA aims to control the spread of solutions in the populations to maintain the genetic diversity for longer time.
  arXiv  Detail & Related papers  (2021-07-18T00:10:15Z)
• Interpreting and Unifying Graph Neural Networks with An Optimization Framework [47.44773358082203]
  Graph Neural Networks (GNNs) have received considerable attention on graph-structured data learning. In this paper, we establish a surprising connection between different propagation mechanisms with a unified optimization problem. Our proposed unified optimization framework, summarizing the commonalities between several of the most representative GNNs, opens up new opportunities for flexibly designing new GNNs.
  arXiv  Detail & Related papers  (2021-01-28T08:06:02Z)

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.