Towards a theory of algorithm flexibility--论文代写范文精选

灵活的核心算法,能够广泛适应不同的问题，提供了基础条件的概念，理解算法的灵活性。这个概念与生物进化有许多相似之处。例如它已被证明,绝大多数现存物种共享一组守恒的规律和过程。下面这篇essay代写范文进行简述。

Lessons from nature 
Although the concept of algorithm flexibility is straightforward, the conditions that dictate whether an MH is flexible are much less obvious and need to be more deeply explored. Along these lines, we feel it is important to make progress in answering the following (related) questions: 
• Plasticity to environmental (problem) context: Are there general conditions that make it easy/difficult to successfully incorporate domain knowledge into an algorithm? 
• Robustness to internal (algorithm) context: Are there general conditions where the inclusion of a particular operator or a design change has a catastrophic impact on other important search characteristics of the algorithm? 
• Origins of design innovation: When is it possible to combine algorithm “building blocks” in new ways to achieve a more effective search process for a specific problem? Below we propose some qualitative attributes that one would expect in an algorithm that is readily adaptable to different optimization contexts. 
• robust yet adaptable behavior: Particular search characteristics do not demand highly specified algorithm conditions 10 (robust) but at the same time these search characteristics can be changed and fine-tuned when needed (adaptable). 
• modularity and loose coupling: Different aspects of the algorithm design can be added or removed while the others can robustly maintain their functionality. More generally, there are little requirements that one feature of the algorithm design places on other design features or on the problem definition. 
• Responsive: Algorithm changes are easy to make and easy to test. Learning by doing is rapid such that the time needed to adapt the algorithm to a local context is fast enough to make learning by doing a viable approach. 
• Feedback: Useful feedback information is available to tell us when things are going right and when things need to be changed. Furthermore, feedback information should provide some guidance about what aspects of the algorithm design may need to change. 
• Simple: Adapting the algorithm design can proceed without expertise in optimization. Similarly, integrating domain knowledge is straightforward to achieve through experimentation and does not require intimate knowledge of the algorithm framework.

The qualities of an adaptive algorithm listed above describe several features that are present in naturally evolving biological systems. For instance, in a review by Kirschner and Gerhart [22], they highlight modularity, loose coupling, component versatility, and exploratory behavior as being highly relevant to the adaptability of a system. The notion of a robust yet flexible algorithmic core that can broadly adapt to different problem conditions provides the basis of our conceptual understanding of algorithm flexibility. 

This conceptual model shares many similarities with observations of biological evolution [22]. For instance, it has been shown that the vast majority of extant species share a set of conserved core systems and processes [23, 24]. Although individual species are highly sophisticated specialists operating within unique environments, they share many internal similarities. The most obvious and universal of these include the underlying principles governing natural evolution, which clearly constitute an astounding generalist. This view of natural evolution mirrors our coarse illustration of algorithm utility in Figure 5b, where we have robust algorithmic frameworks that can be exploited and modified to fit a broad range of distinct optimization conditions. 

Finally, it is worth noting that there have been recent advances in our understanding of the relationship between robustness and adaptation in the context of biological evolution and artificial life [25] [26] [27] [28]. These advances could provide new insights into the design principles that are needed to create more flexible and robust algorithms. For instance, recent studies by this author [27] [28] have provided evidence that a partial overlap in the functionality of components within a system can provide levels of versatility and adaptability that are completely unexpected based on the capabilities of the individual parts. 11 Evidence has also been given that particular system design principles can reconcile the conflicting needs of adaptability and robustness and can lead to systems with evolvable fitness landscapes [28]. This, along with other theoretical studies, may ultimately lead to a deeper understanding of the principles governing algorithm flexibility.

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)

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