### 数学代写|优化算法作业代写optimisation algorithms代考| Nature’s Resum ´ e

statistics-lab™ 为您的留学生涯保驾护航 在代写优化算法optimisation algorithms方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写优化算法optimisation algorithms代写方面经验极为丰富，各种代写优化算法optimisation algorithms相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础
• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 数学代写|优化算法作业代写optimisation algorithms代考|Optimisation by Natural Selection

If we momentarily restrict our attention to the biological branch of nature, we can highlight some of the useful characteristics of this plentiful supplier of inspiration. Undoubtedly the most important contribution to modern biology was made by Charles Darwin with his Theory of Evolution by Natural Selection. Observing the achievements of animal husbandry, he writes:
Why, if man can by patience select variations most useful to himself, should nature fail in selecting variations useful, under changing conditions of life, to her living products … I can see no limit to this power, in slowly and beautifully adapting each form to the most complex relations of life. [17]
The immense explanatory power of a relatively simple set of rules-reproduction, mutation and selection-has often earned Darwin’s theory the title of the most significant scientific discovery of the 19th century. As the evolutionary biologist Theodosius Dobzhansky writes, “nothing in biology makes sense except in the light of evolution.” [19]

The period between the birth of an organism and the birth of its offspring can be decades. The optimality of its behaviour during this period will influence the

likelihood of its genes being propagated. We should not then be surprised to find evolution producing numerous ‘optimisation sub-processes’ suited to different timescales. To achieve this, organisms use various mechanisms to interact with their environment, which may be of use to an algorithm designer. Not only is natural selection itself a source of much inspiration, but it is also key to the existence of all the biological problem-solving mechanisms we find in nature. Accordingly, a solid understanding of evolution by natural selection is of use to any researcher interested in nature-inspired techniques.

If we are to extract an optimisation method from nature, it seems appropriate to ask exactly what nature was using it for. What is being optimised by natural selection? Is there some approximation to an objective function? How is the problem constrained? How can we measure success?

Ants make up 10 percent of the biomass of all animals in the Amazon rain forest [53], but that does not necessarily mean they are a superior solution. Should we measure success by the longevity of the gene? the individual? or perhaps the species? Maybe the efficiency of energy use is important? In The Diversity of Life, E. O. Wilson writes,
The hallmark of life is this: a struggle among an immense variety of organisms weighing next to nothing for a vanishingly small amount of energy.

The natural world is not a stagnant place; meteorological events, tidal forces, plate tectonics, and all the biological activities. Evolution by natural selection is a dynamic process, where the fitness landscape is always changing. As individuals and populations search for new ways to exploit their environment, the environment changes. For example, if a species becomes too skilled at hunting a certain prey, the food supply may run out. To survive, organisms must be able to cope with changing environmental conditions. This change can occur over millennia, a few generations, an individual’s lifetime, or in an instant.

Some organisms have the ability to withstand large variations in the environment. This approach can be thought of as change tolerance, or robustness. Other organisms respond to change more dynamically, using a process called adaptation.
In the most general sense, adaptation is a feedback process in which external changes in an environment are mirrored by compensatory internal changes in an adaptive system. [23]
Nature has been observed to achieve this adaptive ability in many ways, and biologists will undoubtedly continue to discover new mechanisms in the future. An important feature of any adaptive process is some form of memory, either implicit or explicit. Memory allows previous experience to influence future actions.

Closely related to memory is the concept of a learning mechanism. Learning mechanisms process experience and store it in memory. This ability is clearly seen in the human brain, although the mechanism is still poorly understood [38]. A less obvious example is the human immune system, which is capable of recognising and combating infectious foreign elements with specialised responses based on previous exposure .

## 数学代写|优化算法作业代写optimisation algorithms代考|Generality

Many of the nature-inspired algorithms currently in use are being applied to a wide range of problems. This puts them in the category of metaheuristics, where little or no problem specific information is used in the design of the algorithm. But is this kind of generality found in nature, or is it a human innovation?

Generality is related to the concept of adaptability. Some problem-solving mechanisms found in nature can be viewed as hierarchic algorithms. A successful high level algorithm will often use various adaptive subroutines. For example, ants build nests in many different environments, using the most suitable available materials. As generations pass they may adjust to better collect local materials, but the general rules of assembly are retained. This can be tied back to the use of diversity

as a means of preservation. A species which survives only in a very small niche is far more likely to suffer extinction when the environment changes. On the other hand some degree of specialisation will be advantageous, especially during periods of stability. As such, natural selection must find a balance between generality and specialisation.

Natural selection itself is certainly a widespread process in nature, capable of finding novel and elaborate solutions to a huge number of problems. Accordingly, it is not surprising that the evolutionary algorithms have been so broadly and successfully applied [27].

It is interesting to consider natural algorithms in terms of the No Free Lunch Theorems [54]. Since all problem-solving techniques found in nature are to some extent specialised to real problems, there is at least an intuitive reason to think they will perform better than random search on the set of problems arising from real world situations.

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## MATLAB代写

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