Cartesian Genetic Programming Approach for Designing Convolutional Neural Networks
The present study covers an approach to neural architecture search (NAS) using Cartesian genetic programming (CGP) for the design and optimization of Convolutional Neural Networks (CNNs). In designing artificial neural networks, one crucial aspect of the innovative approach is suggesting a novel neural architecture. Currently used architectures have mostly been developed manually by human experts, which is a time-consuming and error-prone process. In this work, we use pure Genetic Programming Approach to design CNNs, which employs only one genetic operation, i.e., mutation. In the course of preliminary experiments, our methodology yields promising results.
Code (0)
등록된 구현이 없습니다.
Tasks
Neural Architecture SearchSimilar Papers 제목 키워드 기반
A Genetic Programming Approach to Designing Convolutional Neural Network Architectures
The convolutional neural network (CNN), which is one of the deep learning models, has seen much success in a variety of computer vision tasks. However, designing CNN architectures still requires expert knowledge and a lo…
General Classificationimage-classificationImage ClassificationNeural Architecture SearchContinuous Cartesian Genetic Programming based representation for Multi-Objective Neural Architecture Search
We propose a novel approach for the challenge of designing less complex yet highly effective convolutional neural networks (CNNs) through the use of cartesian genetic programming (CGP) for neural architecture search (NAS…
Evolutionary AlgorithmsNeural Architecture SearchTowards Advanced Phenotypic Mutations in Cartesian Genetic Programming
Cartesian Genetic Programming is often used with a point mutation as the sole genetic operator. In this paper, we propose two phenotypic mutation techniques and take a step towards advanced phenotypic mutations in Cartes…
regressionSymbolic RegressionImproving Evaluation of Recombination-based Cartesian Genetic Programming
Cartesian Genetic Programming has traditionally been using mutation as its main and often sole genetic operator to drive evolutionary search. Despite advancements in recent years, recombinationbased approaches have long …
Positional Cartesian Genetic Programming
Cartesian Genetic Programming (CGP) has many modifications across a variety of implementations, such as recursive connections and node weights. Alternative genetic operators have also been proposed for CGP, but have not …
Form