Author: Zen and the Art of Computer Programming
In recent years, with the rise of neural network technology, deep learning models have made great progress in the field of natural language processing (NLP). One of the most popular methods at present - Transformer-based NLP model has achieved breakthrough results. However, these models rely on massive data sets for training, so they usually can only solve general text generation tasks. With the emergence of large-scale data and increasingly powerful computing power, improvements to its training process are also indispensable. Based on the above background, in order to further improve the quality of text generation models, researchers have developed some new text generation methods based on evolutionary algorithms, such as template trees and greedy algorithms. Since the existing evolutionary algorithms are only algorithms that combine population mutation and crossover, they have not been applied to text generation models. Therefore, this paper aims to discuss the working principle of text generation model based on evolutionary algorithm and its practical application in natural language generation.
2. Explanation of basic concepts and terms
(1) What is an evolutionary algorithm?
Refers to computer algorithms used to solve optimization problems, the goal of which is to find the global optimal solution in a limited time. Evolutionary algorithms can be used to solve problems such as search, motion planning, genetic algorithm, simulated annealing, and ant colony algorithm.
(2) What is a text generation model?
In computer science, a text generation model is an algorithm required to generate a specific pattern or seed string. It includes character-level models, vocabulary-level models, sentence-level models, and document-level models.
(3) What is the differential evolution algorithm?
It is an optimization algorithm based on evolutionary algorithm, which is used to solve complex