进化神经网络研究综述

进化算法(EAs)与神经网络(NN)的结合已形成了一个新的领域一进化神经网络,在神经网络的研究中举足轻重。本文通过讨论和总结进化神经网络中的关键技术和现状，概述了其设计与构造的趋势。所讨论的是：(1)进化神经网络的研究方法；(2)进化模型；(3)应用实例及关键技术；(4)研究方向。     

进化神经网络研究进展

进化神经网络是将进化算法应用于神经网络的构造、学习而得到的神经网络，具有很强的鲁棒适应性。综述了进化神经网络方法及其应用研究新进展，对研究中出现的一些问题进行了讨论与展望。     

基于量子进化算法的神经网络及应用

研究了量子进化算法,并将其与神经网络相融合,提出了基于量子进化算法的神经网络学习算法,然后将其应用于工业过程丙烯腈收率建模,结果表明量子进化神经网络建模具有较快的收敛速度和较高的模型精度,可以满足工业中要求丙烯腈收率误差不超过1%的要求。     

神经网络结构设计的准则和方法

神经网络结构设计一直是一个很有意义但又难以解决的问题，文中回顾并总结了近年来神经网络结构设计的研究状况。首先分析了在神经网络结构设计中应考虑的４个准则，即神经网络的函数逼近误差、网络结构的复杂性、网络的泛化能力和网络的容错性，随后从构造法、删除法和进化方法３个方面介绍了各种神经网络的设计，并提出了今后研究的展望。     

基于进化计算的神经网络设计与实现

基于进化算法可有产解决神经网络设计和实现中存在的一些问题,使网络具有更优的性能。在此对基于进化计算的神经网络设计和实现的研究内容及进展情况进行综述,讲座了网络实现的关键问题,包括网络权重的进化训练,网络结构进化设计,学习规则进化选取以及进化操作算子设计等,并分析了相关的研究和发展方向。     

基于遗传算法的神经网络性能优化

遗传算法是一种典型的进化算法。文中分析了遗传算法的特点和神经网络的特点,从而得出了把两种算法结合起来进行应用的思想。运用理论对比的方法,阐明了用遗传算法进行神经网络性能优化的原因,并得出结论,认为用遗传算法进行神经网络性能优化促使了神经网络更进一步的应用。阐述了遗传算法优化神经网络的两种主要方法,论述了遗传算法和神经网络的发展现状和将来的研究动向。     

进化神经网络的编码方法与应用

进化神经网络将进化算法与人工神经网络进行了有机结合,进化算法的参与使神经网络系统在进化发育过程中可自适应的进行网络结构与连接权值的调整,改善了神经网络在模拟仿真过程中自主智能化不足的缺陷,提高了神经网络系统的生物真实性。随着研究的深入,大量不同类型的进化神经网络相继出现,根据基因编码方式的不同,可将进化神经网络分为直接编码型和间接编码型两类。本文对神经网络中基因的编码方式进行了阐述分析,最后总结了间接编码方法的应用领域。     

基于神经网络的进化机器人组合行为方法研究

为了克服传统机器人设计方法存在的局限性,提高机器人的自适应能力,采用神经网络方法实现了进化机器人避碰、趋近及其组合行为学习,首先,提出了新的机器人模拟环境和机器人模型,结合了采用神经网络实现进化学习系统的方法。其次,对具有进化学习机制的机器人基本行为和组合行为学习系统进行了仿真,并通过仿真证明了新模型不要求环境知识的完备性,机器人具有环境自适应学习能力,还具有结构简洁、易扩展等特点,最后,对仿真结果进行分析与讨论,并提出了进一步研究方向。     

优化进化神经网络的空气质量预测研究

进化神经网络模型与问题内部机制无关,避免了神经网络收敛到局部,但模型存在参数多而过于复杂的问题。对影响基本进化神经网络模型性能的个体编码方式和适应度函数进行优化,并自适应性定义种群交叉率、变异率。以大气中主要污染物SO2为例,考虑气温、相对湿度、风速等影响因子,实验仿真结果表明优化后的进化神经网络较传统的基本进化神经网络模型进化过程收敛更快,预测效果更佳,为环境保护部门提供可靠的决策依据。     

基于遗传算法的神经网络性能优化

遗传算法是一种典型的进化算法。文中分析了遗传算法的特点和神经网络的特点，从而得出了把两种算法结合起来进行应用的思想。运用理论对比的方法，阐明了用遗传算法进行神经网络性能优化的原因，并得出结论，认为用遗传算法进行神经网络性能优化促使了神经网络更进一步的应用。阐述了遗传算法优化神经网络的两种主要方法，论述了遗传算法和神经网络的发展现状和将来的研究动向。     

Speeding up backpropagation using multiobjective evolutionary algorithms

The use of backpropagation for training artificial neural networks (ANNs) is usually associated with a long training process. The user needs to experiment with a number of network architectures; with larger networks, more computational cost in terms of training time is required. The objective of this letter is to present an optimization algorithm, comprising a multiobjective evolutionary algorithm and a gradient-based local search. In the rest of the letter, this is referred to as the memetic Pareto artificial neural network algorithm for training ANNs. The evolutionary approach is used to train the network and simultaneously optimize its architecture. The result is a set of networks, with each network in the set attempting to optimize both the training error and the architecture. We also present a self-adaptive version with lower computational cost. We show empirically that the proposed method is capable of reducing the training time compared to gradient-based techniques.     

基于统计分析的分阶段进化神经网络方法

基于统计分析和分阶段进化，提出一种新的进化神经网络设计方法．本文方法 的进化过程分三个阶段：第一阶段，首先按训练样本统计特性设计较小规模的神经网络；第 二阶段，引入所有训练样本，在第一阶段的基础上，逐步扩展网络结构，新添加的神经元总 是单独训练并以抵消原网络的输出误差为其训练目标，直至训练网络达到误差要求．第三阶 段，利用统计方法，将网络中非线性变换作用相似的神经元合并，简化网络结构．本文方法 一方面减轻了进化算法的压力,另一方面指出了网络进化的方向使得进化网络的学习过程不 再是黑箱问题．计算机仿真实验表明，该方法是有效的．     

遗传算法在人工智能领域中的应用

该文介绍了遗传算法的基本概念、基本遗传算法的特点和基本遗传算法的求解步骤,同时也介绍了遗传算法在机器学习、并行处理、人工生命以及遗传算法与进化规则及进化策略的结合的发展动向,最后讨论了基于遗传算法的人工神经网络学习中的应用研究,具体论述了遗传算法在学习神经网络权重和学习神经网络拓扑结构的应用方法。     

Evolutionary artificial neural networks: a review

This paper reviews the use of evolutionary algorithms (EAs) to optimize artificial neural networks (ANNs). First, we briefly introduce the basic principles of artificial neural networks and evolutionary algorithms and, by analyzing the advantages and disadvantages of EAs and ANNs, explain the advantages of using EAs to optimize ANNs. We then provide a brief survey on the basic theories and algorithms for optimizing the weights, optimizing the network architecture and optimizing the learning rules, and discuss recent research from these three aspects. Finally, we speculate on new trends in the development of this area.     

On the automated,evolutionary design of neural networks: past,present, and future

Neuroevolution is the name given to a field of computer science that applies evolutionary computation for evolving some aspects of neural networks. After the AI Winter came to an end, neural networks reemerged to solve a great variety of problems. However, their usage requires designing their topology, a decision with a potentially high impact on performance. Whereas many works have tried to suggest rules-of-thumb for designing topologies, the truth is that there are not analytic procedures for determining the optimal one for a given problem, and trial-and-error is often used instead. Neuroevolution arose almost 3 decades ago, with some works focusing on the evolutionary design of the topology and most works describing techniques for learning connection weights. Since then, evolutionary computation has been proved to be a convenient approach for determining the topology and weights of neural networks, and neuroevolution has been applied to a great variety of fields. However, for more than 2 decades neuroevolution has mainly focused on simple artificial neural networks models, far from today’s deep learning standards. This is insufficient for determining good architectures for modern networks extensively used nowadays, which involve multiple hidden layers, recurrent cells, etc. More importantly, deep and convolutional neural networks have become a de facto standard in representation learning for solving many different problems, and neuroevolution has only focused in this kind of networks in very recent years, with many works being presented in 2017 onward. In this paper, we review the field of neuroevolution during the last 3 decades. We will put the focus on very recent works on the evolution of deep and convolutional neural networks, which is a new but growing field of study. To the best of our knowledge, this is the best survey reviewing the literature in this field, and we have described the features of each work as well as their performance on well-known databases when available. This work aims to provide a complete reference of all works related to neuroevolution of convolutional neural networks up to the date. Finally, we will provide some future directions for the advancement of this research area.

     

基于模糊进化规划和分层方法的神经网络设计方法

本文提出一种模糊进化规划，用于前向神经网络的设计．该方法通过对神经元的部分解群体的进化，缩短了个体的编码长度，显著地减轻了计算量，同时这种方法不但能够在很大程度上简化适应值的计算，更重要的是能够降低适应值空间的复杂性，从而能够加速进化算法收敛到全局最优点．仿真结果显示，本文提出的算法能够有效抑制进化规划算法初期收敛的发生，有效地提高多层前向神经网络收敛精度，并可获得更为简洁的网络结构．     

Evolution of an artificial neural network based autonomous landvehicle controller

This paper presents an evolutionary method for creating an artificial neural network based autonomous land vehicle controller. The evolved controllers perform better in unseen situations than those trained with an error backpropagation learning algorithm designed for this task. In this paper, an overview of the previous connectionist based approaches to this task is given, and the evolutionary algorithms used in this study are described in detail. Methods for reducing the high computational costs of training artificial neural networks with evolutionary algorithms are explored. Error metrics specific to the task of autonomous vehicle control are introduced; the evolutionary algorithms guided by these error metrics reveal improved performance over those guided by the standard sum-squared error metric. Finally, techniques for integrating evolutionary search and error backpropagation are presented. The evolved networks are designed to control Carnegie Mellon University's NAVLAB vehicles in road following tasks.