遗传算法的收敛性研究

遗传算法的收敛性分析是遗传算法研究中的重要问题,直接关系到遗传算法的实际应用价值。给出遗传算法全局收敛性的定义,描述当前遗传算法收敛性分析的主要模型,对自适应遗传算法、并行遗传算法、小生境遗传算法等典型遗传算法的收敛性进行分析,给出相关的研究结果,并指出遗传算法收敛性研究的未来发展方向。研究结果对提高遗传算法收敛性具有参考价值。     

并行遗传算法的研究及应用进展

并行遗传算法（PGA）将并行计算机的高速并行性和遗传算法天然的并行性相结合,极大地促进了遗传算法的研究与应用。该文对近年来并行遗传算法的模型、性能分析、算法改进、实现平台进行了归纳和评述,并且对并行遗传算法今后的主要研究方向和发展前景进行了展望。     

并行遗传算法的研究及应用进展

并行遗传算法(PGA)将并行计算机的高速并行性和遗传算法天然的并行性相结合,极大地促进了遗传算法的研究与应用。该文对近年来并行遗传算法的模型、性能分析、算法改进、实现平台进行了归纳和评述,并且对并行遗传算法今后的主要研究方向和发展前景进行了展望。     

量子遗传算法研究进展

针对量子遗传进行了研究,介绍了量子遗传算法的发展、基本理论和方法,从量子门的改进、加入新算子、量子遗传算法的并行性、混合量子遗传算法四个角度论述了量子遗传算法的改进方法,并总结了量子遗传算法的应用领域。最后提出了量子遗传算法的发展方向。     

免疫遗传算法及在优化问题中的应用综述*

指出遗传算法的不足,将免疫学原理引入遗传算法,进而形成免疫遗传算法。针对免疫遗传算法在优化问题中的研究现状,从编码技术、先验知识、操作算子、混沌理论引入、多种群方式、与小生境理论结合等方面进行了总结,指出了不足之处,最后探讨了免疫遗传算法需要进一步研究的问题和发展方向。     

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。     

遗传算法研究进展*

对遗传算法编码策略、遗传算子、参数确定、收敛性、欺骗问题等理论在国内外的研究现状进行了系统的研究,并对遗传算法在国内外的研究进展和新的应用领域进行了讨论;最后,通过对近几年研究文献的统计分析,探讨了遗传算法的研究热点和发展方向。     

图像识别处理中的遗传算法分析

图像识别处理研究领域,遗传算法在优化计算方面发挥着重要作用,目前已在图像恢复、几何形状识别、图像边缘特征提取和图像分割等方面发挥着重要作用。基于此,以图像识别处理中的遗传算法为研究对象,简单阐述分析了遗传算法概念,讨论了遗传算法的特点,重点探讨分析了图像识别处理中遗传算法的具体应用,以期为相关人士提供参考。     

遗传算法理论综述

通过遗传算法的描述,介绍了有关遗传算法理论的研究现状,如:编码方法、适应值函数、遗传算子、参数的选择、数学基础、欺骗问题、收敛性分析、并行计算、局部改进和混合方法等。尽管遗传算法理论基础的不完善使其在应用中还存在某些问题,但通过遗传算法的理论研究将有助于遗传算法理论及其应用的发展。     

遗传算法理论及其应用研究进展

首先阐述遗传算法的原理和求解问题的一般过程;然后讨论了近年来从遗传算子、控制参数等方面对遗传算法的改进,并对遗传算法在计算机科学与人工智能、自动控制以及组合优化等领域的应用进行陈述;最后评述了遗传算法未来的研究方向和主要研究内容。     

Scalable Parallel Genetic Algorithms

Genetic algorithms, search algorithms based on the genetic processes observed in natural evolution, have been used to solve difficult problems in many different disciplines. When applied to very large-scale problems, genetic algorithms exhibit high computational cost and degradation of the quality of the solutions because of the increased complexity. One of the most relevant research trends in genetic algorithms is the implementation of parallel genetic algorithms with the goal of obtaining quality of solutions efficiently. This paper first reviews the state-of-the-art in parallel genetic algorithms. Parallelization strategies and emerging implementations are reviewed and relevant results are discussed. Second, this paper discusses important issues regarding scalability of parallel genetic algorithms.     

遗传算法解柔性作业车间调度问题应用综述

柔性作业车间调度问题是典型的NP难问题,对实际生产应用具有指导作用。近年来,随着遗传算法的发展,利用遗传算法来解决柔性作业车间调度问题的思想和方法层出不穷。为了促进遗传算法求解柔性作业车间调度问题的进一步发展,阐述了柔性作业车间调度问题的研究理论,对已有改进方法进行了分类,通过对现存问题的分析,探讨了未来的发展方向。     

基于遗传算法的混合优化策略研究

最优化问题是工程设计、科学研究、经济管理等众多领域经常遇到的一类问题。随着待解决问题范围的不断扩大以及优化算法研究的不断深入,混合优化策略已成为解决大规模、高复杂度优化问题的一种重要而有效的方法。介绍了遗传算法、贪婪法、模拟退火算法、禁忌搜索的基本原理,阐述了各种算法的优缺点;针对各单一算法存在的缺陷和不足,对三种以遗传算法为主体框架的混合优化算法进行了分析;最后,指出了混合优化算法存在的问题及今后的发展方向。     

A review of the current applications of genetic algorithms in assembly line balancing

Most of the problems involving the design and plan of manufacturing systems are combinatorial and NP-hard. A well-known manufacturing optimization problem is the assembly line balancing problem (ALBP). Due to the complexity of the problem, in recent years, a growing number of researchers have employed genetic algorithms. In this article, a survey has been conducted from the recent published literature on assembly line balancing including genetic algorithms. In particular, we have summarized the main specifications of the problems studied, the genetic algorithms suggested and the objective functions used in evaluating the performance of the genetic algorithms. Moreover, future research directions have been identified and are suggested.     

遗传程序设计方法综述

近年来,遗传程序设计（genetic programming,GP)的研究引起了人们很大的关注,它运用遗传算法（genetic algorithm,GA)的思想,通过生成计算机程序来解决问题,介绍了遗传程序设计的研究状况以及目前的研究进展,概述了它的基本算法、主要特点、理论与技术,同时介绍了一些GP实现系统以及主要的应用领域,最后探讨了遗传程序设计的研究方向。     

Effects of different chromosome representations in developing genetic algorithms to solve DFJS scheduling problems

This paper attempts to compare the effect of using different chromosome representations while developing a genetic algorithm to solve a scheduling problem called DFJS (distributed flexible job shop scheduling) problem. The DFJS problem is strongly NP-hard; most recent prior studies develop various genetic algorithms (GAs) to solve the problems. These prior GAs are similar in the algorithmic flows, but are different in proposing different chromosome representations. Extending from this line, this research proposes a new chromosome representation (called S_OP) and develops a genetic algorithm (called GA_OP) to solve the DFJS problem. Experiment results indicate that GA_OP outperforms all prior genetic algorithms. This research advocates the importance of developing appropriate chromosome representations while applying genetic algorithms (or other meta-heuristic algorithms) to solve a space search problem, in particular when the solution space is high-dimensional.     

Hybrid multi-objective shape design optimization using Taguchi’s method and genetic algorithm

This research is based on a new hybrid approach, which deals with the improvement of shape optimization process. The objective is to contribute to the development of more efficient shape optimization approaches in an integrated optimal topology and shape optimization area with the help of genetic algorithms and robustness issues. An improved genetic algorithm is introduced to solve multi-objective shape design optimization problems. The specific issue of this research is to overcome the limitations caused by larger population of solutions in the pure multi-objective genetic algorithm. The combination of genetic algorithm with robust parameter design through a smaller population of individuals results in a solution that leads to better parameter values for design optimization problems. The effectiveness of the proposed hybrid approach is illustrated and evaluated with test problems taken from literature. It is also shown that the proposed approach can be used as first stage in other multi-objective genetic algorithms to enhance the performance of genetic algorithms. Finally, the shape optimization of a vehicle component is presented to illustrate how the present approach can be applied for solving multi-objective shape design optimization problems.     

遗传算法理论研究综述

针对遗传算法在理论研究方面存在的不足,系统地讨论了遗传算法理论研究的主要内容和方法,包括模式定理、编码策略、Ｍａｒｋｏｖ链与全局收敛性、维数分析、ＢＧＡ理论、可分离函数、Ｗａｌｓｈ与傅立叶函数分析及二次动力系统等,介绍了Ｎｏ Ｆｒｅｅ Ｌｕｎｃｈ定理,并指出相关的研究方向。     

A review: accuracy optimization in clustering ensembles using genetic algorithms

The clustering ensemble has emerged as a prominent method for improving robustness, stability, and accuracy of unsupervised classification solutions. It combines multiple partitions generated by different clustering algorithms into a single clustering solution. Genetic algorithms are known as methods with high ability to solve optimization problems including clustering. To date, significant progress has been contributed to find consensus clustering that will yield better results than existing clustering. This paper presents a survey of genetic algorithms designed for clustering ensembles. It begins with the introduction of clustering ensembles and clustering ensemble algorithms. Subsequently, this paper describes a number of suggested genetic-guided clustering ensemble algorithms, in particular the genotypes, fitness functions, and genetic operations. Next, clustering accuracies among the genetic-guided clustering ensemble algorithms is compared. This paper concludes that using genetic algorithms in clustering ensemble improves the clustering accuracy and addresses open questions subject to future research.