An effective work/research proposal consists of the following elements:
ü Setting of work proposal工作提案建構 : 你工作提案的主題是什麼? 你的讀者可以明瞭工作提案的內容嗎?
ü Work problem工作問題 : 你的工作提案裡有你試著要解決或是想更進一步瞭解的問題嗎?
ü Quantitative specification of problem 問題的量化 : 你要如何量化問題來讓你的讀者明白之前文獻研究所遇到的量化限制 ?
ü Importance of problem問題的中心 : 如果問題沒被解決或是充分瞭解, 這對工作提案的讀者會有多大的負面衝擊?
ü Work objective工作目標 : 工作提案的目標 ?
ü Methodology to achieve objective 達成目標的方法 : 你的計劃中達成目標的步驟?
ü Anticipated results希望的結果 : 你希望達成的結果?
ü Contribution to field領域的貢獻: 你的提案對相關工作領域的貢獻?
Read the following work proposal from Writing Effective Work Proposals by Ted Knoy for a computer science-related (資訊科學相關分類) project:
Many machine learning and optimization application-related
[ ( xx )
problems are solved by genetic algorithms (GAs) with the
multi-state property. For instance, in chess, a good player often
] [ ( ii )
employs various strategies based on his opponent's moves, the
game's progress, or the chess clock. Therefore, an intelligent
chess playing program should consider the multi-state property
to perform more effectively. Consider stock market
investments as another example. Investors adopt various
strategies according to whether the market is up or down. This
behavior implies that a decision support system for investment
should consider the multi-state property. Applying various
strategies to distinct states of a problem is natural. A
different solution or strategy should be employed by varying
problem solving states to achieve the global optimum. However,
] [
conventional methods cannot solve multi-state problems.
( xviii )
Although genetic algorithms are widely applied to machine
learning and optimization, conventional GAs have not received
much attention. Conventional approaches can use human
]
designed rule bases to represent a multi-state solution, and
employ GAs to alter the rule base. However, to our knowledge,
no systematic method has been developed for dealing with the
multi-state property in a GA-implemented system. If the
[
solution varies with the problem state in a multi-state problem,
( iv )
conventional methods neglect the multi-state property and yield
an inaccurate and unfeasible solution. For example, an
] [ ( vi )
investment decision support system that adopts a single
strategy, regardless of whether the market is up or down, leads
to an unsuccessful investment. Although a rule base can be
] [ ( xvi )
used to represent the multi-state property, designing the rule
base requires many manhours.
Based on the above, we should design an effective GAs model
[ ( viii )
capable of deriving an optimal solution for each state in a
multi-state problem. To do so, the proposed fuzzy polyploidy, a
] [ ( xiv )
multi-state chromosome coding scheme, can be used to describe
the solution of a multi-state problem. An adaptive genetic
structure model can then be adopted to derive an appropriate
polyploidy structure for practical applications. The proposed
model consists of three structural level operations, including
structural expansion, structural deletion, and structural
coercion, to simulate the natural random variation.
As anticipated, the proposed model can increase the accuracy
[ ( x )
of the optimum solution derived for a particular multi-state
problem. Restated, a deeper multi-state property of a problem
implies a more accurate solution achievable by the proposed
model. For applications with only a signal state (without the
multi-state property), the proposed model is comparable to
conventional GAs and only requires a small amount of extra
memory and computational time. Moreover, the proposed GA
method can enhance conventional genetic algorithms by
systematically solving multi-state problems through the use of
the polyploidy concept. Polyploidy encoding observed in nature
] [ ( xii )
provides a more flexible and dynamic encoding scheme than do
conventional approaches. In practice, genetic algorithms have
difficulty in obtaining optimum solutions when the chromosome
structure is too complex because a complex structure always
involves a large search space. The proposed simple to complex
process, accompanied by a polyploidy model, facilitates the
evolution of a complex structure that involves a large search
space.
Match the parts of a work proposal with the sentences in the above proposal.
1. Methodology to achieve objective 達成目標的方法 in the above proposal can be found in
A. x
B. viii
C. xiv
2. Work objective工作目標 in the above proposal can be found in
A. viii
B. xii
C. ii
3. Setting of work proposal工作提案建構 in the above proposal can be found in
B. ii
C. xx
4. Contribution to field領域的貢獻 in the above proposal can be found in
A. xiv
C. viii
5. Work problem工作問題 in the above proposal can be found in
A. xvi
B. vi
C. xviii
6. Anticipated results希望的結果 in the above proposal can be found in
B. xiv
C. xii
7. Quantitative specification of problem 問題的量化 in the above proposal can be found in
A. ii
C. iv
8. Importance of problem問題的中心 in the above proposal can be found in
9. An example of the setting of work proposal工作提案建構 in the above proposal can be found in
A. xii
10. An example of quantitative specification of problem 問題的量化 can be found in
A. xviii
C. x
Score =
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