"突破难题的新工具：摩根·考夫曼人工智能系列"

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The Morgan Kaufmann Artificial Intelligence Series explores the groundbreaking advancements in computational intelligence tools that have revolutionized problem-solving capabilities in the past decade. Artificial neural networks, fuzzy systems, and evolutionary computation are among the key technologies that have emerged as powerful tools for tackling complex problems that were once deemed impossible to solve. These computational intelligence tools have not only matured individually but have also been integrated with traditional approaches like statistical analysis to enhance their problem-solving capabilities. This integration has unlocked new possibilities in various fields, allowing for innovative solutions to the most daunting challenges. The series delves into the intricate workings of these advanced analytical tools, providing insights into how they operate and exploring their applications across diverse industries. From machine learning to data mining, these tools have been instrumental in driving advancements in artificial intelligence and enabling computers to emulate human-like intelligence. As technology continues to evolve, the Morgan Kaufmann Artificial Intelligence Series serves as a valuable resource for researchers, practitioners, and students seeking to stay at the forefront of the latest developments in computational intelligence. By combining theoretical foundations with practical applications, the series offers a comprehensive overview of the capabilities and potential of these transformative technologies. Whether you are a seasoned expert or a newcomer to the field of artificial intelligence, the Morgan Kaufmann Artificial Intelligence Series provides a wealth of knowledge and insights to guide you through the ever-changing landscape of computational intelligence. Join us on this journey of exploration and discovery as we uncover the limitless possibilities of artificial intelligence and its impact on the world we live in.

Chapter OnemFoundations

an organism is called the

genotype.

In evolutionary computation, the collection of

patterns or strings needed to completely specify a system is known as a

structure.

Most of the systems considered in this text are specified by one pattern, or string, or

state vector; the terms

chromosome

and

structure

are thus generally interchangeable.

In the biological world, chromosomes are made up of

genes,

each of which is

identified by its location

(locus)

and its function, such as a person's hair color gene.

In other words, genes are specific segments of chromosomes associated with specific

functions. Individual values a gene may assume are called

alleles;

a hair color allele

value may be "brown hair." In the artificial chromosomes of evolutionary compu-

tation systems, the chromosome patterns or strings are made up of parameters, or

features, that can vary over a specified range of values. A given parameter or feature

occupies a fixed location in the artificial chromosome. The chromosome therefore

is encoded to represent a set of parameters.

Biological versus Artificial Chromosomes

Just as artificial neural networks are only roughly analogous to collections of bio-

logical neurons, so artificial chromosomes are only approximately modeled after

biological ones.

Composition

Biological chromosomes contain linear threads of DNA, nucleic acids that make up

an extremely complex double helix structure. Artificial chromosomes are typically

strings of binary and/or real values. Each occurrence of the string typically represents

a system state vector.

Length

The biological chromosomes that define an organism vary in length, although a spe-

cific chromosome is generally the same length from one organism to another. Each

artificial chromosome in a population is the same length, that is, contains the same

number of bits.

Reproduction

Biological chromosomes duplicate themselves during cell division, which occurs

during a normal cell's lifetime. Many cell divisions (duplications) occur within an

organism for every event of sexual reproduction. During reproduction, the egg and

the sperm each contribute one chromosome for each homologous pair. In evolu-

tionary computation, the duplication of chromosomes analogous to what occurs

during biological cell division is generally called "reproduction." Also, the synthe-

sis of new chromosomes from two "parents" is called crossover, or recombination,

in evolutionary computation. Furthermore, during crossover (or recombination),

any number of bits or real values can be exchanged between two parent artificial

Behavioral Motivations for Fuzzy Logic

chromosomes, as compared with the fixed 50 percent contribution of chromosomes

by each parent in human reproduction. 3

This section has primarily discussed the biological basis of evolutionary compu-

tation from a genetics point of view. Concepts such as survival of the fittest, associ-

ated with Darwinian evolution, also play an important role in CI and are

discussed in Chapter 3.

Behavioral Motivations for Fuzzy Logic

The biological motivation or basis for fuzzy logic does not originate at the cellular

and subcellular level, as is the case with neural networks and evolutionary compu-

tation, respectively. It is reflected at the behavioral level of the organism, that is,

in the ways the organism interacts with its environment. While the previous two

methods are deeply rooted in biology, fuzzy logic deals mainly with uncertainty

and vagueness. We do not live in a world of ones and zeros, black and white, true

and false, or other absolutes. Our observations, communications, and experiences

almost always include a large measure of uncertainty. For example, a statement

such as "Next year I will visit Hawaii" cannot be categorized in terms of truth and

falsehood. It is uncertain.

Two main types of uncertainty exist. One is

statistical,

based on the laws of prob-

ability. An example of statistical uncertainty is the outcome of the toss of a coin.

Observations or measurements can be used to resolve statistical uncertainty. For

example, once the coin is tossed, no statistical uncertainty remains. The other type

of uncertainty is nonstatistical and is based on vagueness, imprecision, and/or ambi-

guity. Nonstatistical uncertainty is illustrated by statements such as "Go to bed

pretty

soon"

and "Jim is

very tall"

and "That car is going

around

75 kilometers per hour."

The concept of fuzziness is associated with nonstatistical uncertainty.

Those of you who are experts in the English language may have noticed that,

particularly in the first statement, the imperative state does not mesh very well with

the vague qualifier "pretty soon." This, however, is exactly the kind of vague, messy

English we often use for communication. One of the primary attributes of fuzzy

logic is its ability to efficiently capture and manipulate these vague, messy concepts.

Fuzziness is an inherent property of a system. It is not resolved or altered by

observation or measurement. Allowing uncertainty in the description of a complex

system makes it more tractable to analysis. Fuzzy logic thus provides a framework

within which nonstatistical uncertainty can be defined, described, and analyzed.

A similar perspective on fuzzy logic is articulated by George Klir (Klir and Folger

1988), who refers to fuzziness as arising from what he calls "linguistic imprecision."

3 For more information on natural genetics, refer to a genetics text. A good choice is one written by

Mange and Mange (1998).

Chapter OnemFoundations

Myths about Computational Intelligence

There are a number of myths regarding computational intelligence. First, it is a myth

that the only way to achieve results with CI tools is with a vast sum of money, a

supercomputer, and an interdisciplinary team of Nobel laureates, as some commer-

cial vendors imply. Having a supercomputer or a parallel processing machine isn't

required to do something useful with CI tools. It's not even necessary to have a

Sun workstation. A personal computer is a perfectly adequate hardware base for

most implementation and application projects. So, with relatively simple hardware

and software tools, it is possible to solve problems that are otherwise impossible or

impractical. Computational intelligence tools

offer solutions to some problems

that aren't feasible to solve in any other way known to the authors. That isn't a myth!

What

a myth is that some combination of CI tools can solve all difficult engi-

neering or computer science problems faster and cheaper than anything previously

available. It is also a myth that CI tools can solve most problems single-handedly.

They are often inappropriate for problems requiring precise calculations. For exam-

ple, it is unlikely that anyone will ever successfully balance a checkbook with a neural

network.

Another statement that qualifies as

mostly

myth is that no programming is

needed to use artificial neural networks. This is at best misleading. It is true that

a neural network trains (adapts) and runs on input data and according to a set of

rules that update the weights that connect the processing elements, or nodes, and

that the learning of the network is not, strictly speaking, programmed. It is also

true that computer-aided software engineering (CASE) tools are becoming more

available and that little or no programming expertise may be required to use these

tools to generate executable neural network code.

It is also true, however, that in the real world of neural network applications,

some programming is required to get from the specification of the problem to

a solution. Neural network applications significantly reduce the requirement for

reprogramming. Once the problem is specified, it is not unusual to reuse the net-

work code repeatedly, making changes in data preprocessing and network runtime

parameters.

Furthermore, although it is accurate to say that computational intelligence tools

such as neural networks can play a key role in the solution of several classes of

problems that are difficult if not impossible to solve any other way currently known,

it is almost always true that the CI portion of the solution is only a relatively

small part of the overall system. For example, in terms of the total amount of

computer code in a neural network-based solution, the network often accounts

for only about 10 percent of the total solution. It is an absolutely indispensable

10 percent, and success would not be possible without it, but it is important to

keep it in perspective. Preprocessing and further manipulation of the data to form

Computational Intelligence Application Areas

pattern files for presentation to the network typically involve much of the code

(although we'll show you a way to develop a neural network that eliminates much

of the preprocessing). Interpreting and displaying the results often account for

another large portion. 4

Another myth about neural network and evolutionary computation applications

is that it is necessary to know something about neural biology or biological genetics,

respectively, to understand them. Nothing could be further from the truth. In fact,

for most engineers and computer scientists, neural network and evolutionary com-

putation tools can be considered just another (powerful) set of resources in the CI

analysis toolkit. Furthermore, a good case can be made for the argument that neural

networks are technical descendants of analog computing just as much as they are

descended from biology or neurology.

A myth about fuzzy logic is that it is really fuzzy, or imprecise. It is not. The inputs

to a fuzzy system are precise values for input parameters. Likewise, outputs from a

fuzzy system are "crisp" (exact) values, capable, for instance, of being used as precise

inputs to control systems.

Another myth about fuzzy logic is that it is just another version of probability. It

isn't. Probability deals with statistical uncertainty, whereas fuzzy logic is related to

nonstatistical uncertainty, as we discussed previously.

Finally, it is a myth that optimization exists. This is being said somewhat with

tongue in cheek, but it is important to realize that very seldom does a real-world CI

implementation find the absolute optimum of anything. It is almost always sufficient

to get within a specified region of the optimum, if it is known. Often, in fact, the

optimum value is not even known. Note that we use the term

optimization

in its

pure "dictionary definition" sense: Optimization is the identification of the very best

solution, or, in the case in which multiple optima exist, the identification of all of

the multiple optima.

Computational Intelligence Application Areas

Each component methodology of computational intelligence has application areas

for which it is particularly well suited. We briefly review these areas in this section.

Keep in mind that application areas may overlap; that is, a given problem may be

solvable by either a neural network or a fuzzy system, albeit with different levels of

performance. In later chapters we examine combinations of the methodologies that

can produce different results. This compilation of application areas is not meant

The 10 percent of the code typically represented by the neural network often takes a dispropor-

tionately large percentage of the development effort, perhaps 20 percent, but that effort associated

directly with neural network application development is usually still a relatively small portion of

the total project.

Chapter OnemFoundations

to be complete. It is not necessarily even representative of all of the major areas of

applications. It

meant to convey some sense of the range of problems to which CI's

component methodologies have been applied.

Neural Networks

There are five application areas for which neural networks are generally considered

to be best suited. The first three are related.

Classification

This area analyzes which of several predefined classes best reflects an input pattern.

The number of classes is typically small compared with the number of inputs. One

example is a decision whether or not a given segment of EEG data represents an

epileptiform spike waveform. Neural networks' ability to construct nonlinear map-

pings between high-dimensional spaces is another type of classification analysis.

Some types of video image processing by neural networks (such as diagnoses of

tumors) are examples of this application area.

Content Addressable Memory or Associative Memory

A typical example is obtaining the complete version of a pattern at the output of the

network by providing a partial version at the input. (The input and output nodes of

the network may sometimes be the same nodes.) This process is sometimes described

as obtaining an exemplar pattern from a noisy and/or incomplete one.

Clustering or Compression

This area involves classification but can also be considered a form of encoding. An

example is the significant reduction of the dimensionality of an input, as in the case

of speech recognition. Another is the reduction of the number of bits that must

be stored or transmitted to represent, within some allowed error margin, a block

of data; in other words, the original block of data can be reconstructed within the

allowed error with fewer bits than were in the original data.

Generation of Sequences or Patterns

This fourth area is somewhat different from the first three in that no classification

is involved. This generation of patterns is done by a network trained to examples.

For instance, if a network is trained to reproduce a certain style of musical sequence,

then it is possible for the network to compose "original" versions of that type of

music. Or a neural network may be trained to model, or simulate, something. Grow-

ing numbers of applications in the financial world, becoming known as "financial

engineering" applications, are being reported. Because of inherent randomness in

the process being simulated, there may be no "right" answers, but the system can

perhaps be described statistically. The network simulation may then be designed

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