dominance of meta-heuristics--论文代写范文精选

这篇essay代写范文提出了MH的假设，推导出灵活的实用程序。这种灵活性支持的经验证据表明,MH设计可以适应环境,可以将领域知识集成。我们建议从搜索算法设计的灵活性，意味着应该存在于一个灵活的算法框架。

Abstract
Although researchers often discuss the rising popularity of meta-heuristics (MH), there has been a paucity of data to directly support the notion that MH are growing in prominence compared to deterministic methods (DM). Here we provide the first evidence that MH usage is not only growing, but indeed appears to have surpassed DM as the algorithm framework of choice for solving optimization problems. Motivated by these findings, this paper aims to review and discuss the origins of meta-heuristic dominance. 

Explanations for meta-heuristic success are varied, however their robustness to variations in fitness landscape properties is often cited as an important advantage. In this paper, we review explanations for MH popularity and discuss why some of these arguments remain unsatisfying. We argue that a more compelling and comprehensive explanation would directly account for the manner in which most MH success has actually been achieved, e.g. through hybridization and customization to a particular problem environment. 

This paper puts forth the hypothesis that MH derive much of their utility from being flexible. This flexibility is empirically supported by evidence that MH design can adapt to a problem environment and can integrate domain knowledge. We propose what flexibility means from a search algorithm design context and we propose key attributes that should exist in a flexible algorithm framework. Interestingly, a number of these qualities are observed in robust biological systems. In light of these similarities, we consider whether the origins of biological robustness, (e.g. loose coupling, modularity, partial redundancy) could help to inspire the development of more flexible algorithm frameworks. We also discuss current trends in optimization problems and speculate that highly flexible algorithm frameworks will become increasingly popular within our diverse and rapidly changing world.

INTRODUCTION 
Data on meta-heuristic usage in public, private, and academic sectors is sparse, however there has been some evidence that their use in computer-based problem solving is growing [1] [2] [3]. On almost a daily basis, there are new nature-inspired algorithms being proposed, new journals and conferences being advertised, as well as a continuous supply of new applications being considered within academic research. In [2], bibliographic data on genetic algorithms is used to show that publications within this field experienced a 40% annual growth from 1978 to 1998. More recently in [1], they present survey data showing that evolutionary computation (EC) usage is growing at a super linear rate in both public and private sectors. D Although these studies clearly indicate a growth in EC usage, it is not clear how these usage trends compare with similar research and development activity in deterministic methods. 

In particular, it has not been determined whether MH growth is actually outpacing alternative optimization techniques. By analyzing data from a number of publically accessible databases, we provide evidence in Box 1 that the usage of meta-heuristics is not only growing, but in many respects meta-heuristics are surpassing deterministic methods as the framework of choice for solving optimization problems. It is clear from the results in Box 1 that the number of optimization publications, case studies, and patents is growing and that this growth is in many ways irrespective of the search paradigm being considered. There are undoubtedly a number of interrelated factors contributing to this growth including technological innovation, global prosperity, as well as a growth in the number of problems that can be solved through computer-based methods, e.g. due to simulation technology and the growing availability of computing resources. However, it is also apparent from Box 1 that meta-heuristic implementation has been growing at a rate that is not matched by deterministic methods. Our aim in this paper is to try to understand why this is happening.

CONCLUSIONS 
Historically optimization problems were not thought of as having an expiration date. However, waning are the days when a problem could be defined and studied for years without the problem changing. More and more in today’s world, new problems rapidly come into existence and existing problems unexpectedly change due to new conditions. Solution quality will always be a primary concern, however the algorithm development time and an algorithm’s capacity to deal with new information and new conditions is expected to become an increasingly valued asset when addressing optimization problems. In this paper, we provided evidence that meta-heuristics such as genetic algorithms are becoming increasingly favoured to solve today’s optimization problems. We proposed that this growing dominance may be the result of an inherent flexibility that allows these algorithms to be efficiently and effectively modified to fit the characteristics of a problem. In other words, MH popularity may have less to do with the efficacy of a particular set of algorithm designs on a particular set of problems and have more to do with the ability of MH (but also the people and culture surrounding their development) to incorporate domain knowledge and to be advantageously combined with other methods.(论文代写)

51Due网站原创范文除特殊说明外一切图文著作权归51Due所有；未经51Due官方授权谢绝任何用途转载或刊发于媒体。如发生侵犯著作权现象，51Due保留一切法律追诉权。(论文代写)
更多essay代写范文欢迎访问我们主页 www.51due.com 当然有essay代写需求可以和我们24小时在线客服 QQ:800020041 联系交流。-X(论文代写)