gatbxa1.ps

王小平《遗传算法——理论、应用与软件实现》随书光盘

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3.09 (A) 412.78 636.95 P
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3.7 (\325) 444.98 636.95 P
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0.17 (analysis, visualisation tools and special purpose application domain toolboxes and) 135.65 622.95 P
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0.85 (A) 332.76 568.95 P
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1.02 ( matrix functions to build a set of) 364.97 568.95 P
0.87 (versatile tools for implementing a wide range of genetic algorithm methods. The) 135.65 554.95 P
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1.04 (oolbox is a collection of routines, written mostly in m-\336les,) 237.48 540.95 P
(which implement the most important functions in genetic algorithms.) 135.65 526.95 T
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(.) 504.71 666.95 T
3.33 (A number of demonstrations are available. A single-population binary-coded) 135.65 640.95 P
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-0.61 (sga.m) 167.04 612.95 P
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-0.25 (. The demonstration m-\336le) 203.02 612.95 P
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-0.61 (mpga.m) 332.93 612.95 P
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-0.25 ( implements a real-valued multi-) 376.1 612.95 P
1.29 (population genetic algorithm to solve a dynamic control problem. Both of these) 135.65 598.95 P
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(Examples) 382.16 584.95 T
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( Section.) 428.79 584.95 T
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1.06 (, a set of test functions, drawn from the genetic algorithm literature,) 195.51 558.95 P
2.55 (are supplied in a separate directory) 135.65 544.95 P
2.55 (,) 315.12 544.95 P
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2.55 (, from the Genetic Algorithm) 381.23 544.95 P
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0.93 ( Section. A further document describes the implementation and use) 200.86 516.95 P
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(An Overview of Genetic Algorithms) 63.65 732.95 T
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-0.06 (In this Section we give a tutorial introduction to the basic Genetic Algorithm \050GA\051) 135.65 694.95 P
(and outline the procedures for solving problems using the GA.) 135.65 680.95 T
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(What ar) 135.65 652.29 T
(e Genetic Algorithms?) 192.66 652.29 T
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0.56 (The GA is a stochastic global search method that mimics the metaphor of natural) 135.65 624.95 P
0.66 (biological evolution. GAs operate on a population of potential solutions applying) 135.65 610.95 P
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0.86 (approximations to a solution. At each generation, a new set of approximations is) 135.65 582.95 P
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2.41 (that are better suited to their environment than the individuals that they were) 135.65 526.95 P
(created from, just as in natural adaptation.) 135.65 512.95 T
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3.17 (chr) 463.14 486.95 P
3.17 (omosomes) 478.68 486.95 P
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3.17 (,) 528.65 486.95 P
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0.8 (genotypes) 357.07 472.95 P
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0.8 ( \050chromosome values\051 are) 405.03 472.95 P
3.66 (uniquely mapped onto the decision variable \050) 135.65 458.95 P
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3.66 (phenotypic) 374.11 458.95 P
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3.66 (\051 domain. The most) 426.73 458.95 P
0.03 (commonly used representation in GAs is the binary alphabet {0, 1} although other) 135.65 444.95 P
1.05 (representations can be used, e.g. ternary) 135.65 430.95 P
1.05 (, integer) 331.97 430.95 P
1.05 (, real-valued etc. For example, a) 371.85 430.95 P
2.13 (problem with two variables,) 135.65 416.95 P
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2.13 (x) 281.76 416.95 P
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1.78 (1) 287.08 413.95 P
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2.13 ( and) 292.08 416.95 P
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2.13 (x) 319.66 416.95 P
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1.78 (2) 324.98 413.95 P
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2.13 (, may be mapped onto the chromosome) 329.98 416.95 P
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-0.3 (x) 167.65 299.98 P
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-0.25 (1) 172.98 296.98 P
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-0.3 ( is encoded with 10 bits and) 177.97 299.98 P
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-0.3 (x) 312.82 299.98 P
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-0.25 (2) 318.14 296.98 P
2 12 Q
-0.3 ( with 15 bits, possibly re\337ecting the level of) 323.14 299.98 P
-0.2 (accuracy or range of the individual decision variables. Examining the chromosome) 135.65 285.98 P
0.43 (string in isolation yields no information about the problem we are trying to solve.) 135.65 271.98 P
0.11 (It is only with the decoding of the chromosome into its phenotypic values that any) 135.65 257.98 P
1.35 (meaning can be applied to the representation. However) 135.65 243.98 P
1.35 (, as described below) 409.1 243.98 P
1.35 (, the) 509.64 243.98 P
0.55 (search process will operate on this encoding of the decision variables, rather than) 135.65 229.98 P
0.8 (the decision variables themselves, except, of course, where real-valued genes are) 135.65 215.98 P
(used.) 135.65 201.98 T
-0.19 (Having decoded the chromosome representation into the decision variable domain,) 135.65 175.98 P
1.94 (it is possible to assess the performance, or) 135.65 161.98 P
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1.94 (\336tness) 356.04 161.98 P
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1.94 (, of individual members of a) 386.03 161.98 P
3.53 (population. This is done through an objective function that characterises an) 135.65 147.98 P
0.6 (individual\325) 135.65 133.98 P
0.6 (s performance in the problem domain. In the natural world, this would) 187.63 133.98 P
0.08 (be an individual\325) 135.65 119.98 P
0.08 (s ability to survive in its present environment. Thus, the objective) 216.42 119.98 P
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(Genetic Algorithm Toolbox User\325s Guide) 63.65 61.61 T
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2.35 (function establishes the basis for selection of pairs of individuals that will be) 135.65 736.95 P
(mated together during reproduction.) 135.65 722.95 T
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0.94 (from its raw performance measure given by the objective function. This value is) 135.65 682.95 P
1.18 (used in the selection to bias towards more \336t individuals. Highly \336t individuals,) 135.65 668.95 P
2.11 (relative to the whole population, have a high probability of being selected for) 135.65 654.95 P
2.56 (mating whereas less \336t individuals have a correspondingly low probability of) 135.65 640.95 P
(being selected.) 135.65 626.95 T
0.54 (Once the individuals have been assigned a \336tness value, they can be chosen from) 135.65 600.95 P
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2.85 (recombined to produce the next generation. Genetic operators manipulate the) 135.65 572.95 P
0.61 (characters \050genes\051 of the chromosomes directly) 135.65 558.95 P
0.61 (, using the assumption that certain) 364.71 558.95 P
0.18 (individual\325) 135.65 544.95 P
0.18 (s gene codes, on average, produce \336tter individuals. The recombination) 187.63 544.95 P
0.68 (operator is used to exchange genetic information between pairs, or lar) 135.65 530.95 P
0.68 (ger groups,) 477.01 530.95 P
4.62 (of individuals. The simplest recombination operator is that of single-point) 135.65 516.95 P
(crossover) 135.65 502.95 T
(.) 180.95 502.95 T
(Consider the two parent binary strings:) 135.65 476.95 T
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(P) 171.65 450.95 T
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(1) 178.84 447.95 T
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( = 1 0 0 1 0 1 1 0) 184.84 450.95 T
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(, and) 314.37 450.95 T
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(P) 171.65 424.95 T
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(2) 178.84 421.95 T
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( = 1 0 1 1 1 0 0 0) 184.84 424.95 T
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(.) 314.37 424.95 T
0.59 (If an integer position,) 135.65 398.95 P
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0.59 (i) 244.27 398.95 P
2 F
0.59 (, is selected uniformly at random between 1 and the string) 247.6 398.95 P
0.97 (length,) 135.65 384.95 P
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0.97 (l) 172.6 384.95 P
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0.97 (, minus one [1,) 175.93 384.95 P
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0.97 (l) 254.42 384.95 P
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0.97 (-1], and the genetic information exchanged between the) 257.76 384.95 P
0.15 (individuals about this point, then two new of) 135.65 370.95 P
0.15 (fspring strings are produced. The two) 351.03 370.95 P
(of) 135.65 356.95 T
(fspring below are produced when the crossover point) 145.42 356.95 T
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(i = 5) 403.22 356.95 T
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( is selected,) 426.64 356.95 T
3 F
(O) 171.65 330.95 T
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(1) 178.84 327.95 T
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( = 1 0 0 1 0 0 0 0) 184.84 330.95 T
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(, and) 314.37 330.95 T
3 F
(O) 171.65 304.95 T
3 10 Q
(2) 178.84 301.95 T
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( = 1 0 1 1 1 1 1 0) 184.84 304.95 T
2 F
(.) 314.37 304.95 T
3.99 (This crossover operation is not necessarily performed on all strings in the) 135.65 278.95 P
0.87 (population. Instead, it is applied with a probability) 135.65 264.95 P
0 F
0.87 (Px) 387.77 264.95 P
2 F
0.87 ( when the pairs are chosen) 400.42 264.95 P
-0.2 (for breeding. A further genetic operator) 135.65 250.95 P
-0.2 (, called mutation, is then applied to the new) 324.02 250.95 P
1.8 (chromosomes, again with a set probability) 135.65 236.95 P
1.8 (,) 347.08 236.95 P
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1.8 (Pm) 354.88 236.95 P
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1.8 (. Mutation causes the individual) 370.87 236.95 P
1.06 (genetic representation to be changed according to some probabilistic rule. In the) 135.65 222.95 P
0.41 (binary) 135.65 208.95 P
0.41 (string) 172.7 208.95 P
0.41 ( representation,) 206.01 208.95 P
0.41 ( mutation will cause a single bit to change its state,) 283.36 208.95 P
0.74 (0) 135.65 194.95 P
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0.74 (\336) 145.38 194.95 P
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0.74 (1 or 1) 160.96 194.95 P
4 F
0.74 (\336) 194.15 194.95 P
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0.74 ( 0. So, for example, mutating the fourth bit of) 205.99 194.95 P
3 F
1.77 (O) 434.89 194.95 P
3 10 Q
1.48 (1) 442.09 191.95 P
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0.74 ( leads to the new) 448.09 194.95 P
(string,) 135.65 180.95 T
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(O) 171.65 154.95 T
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(1m) 178.84 151.95 T
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( = 1 0 0 0 0 0 0 0) 190.84 154.95 T
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(.) 320.37 154.95 T
0.19 (Mutation is generally considered to be a background operator that ensures that the) 135.65 128.95 P
0.61 (probability of searching a particular subspace of the problem space is never zero.) 135.65 114.95 P
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(Genetic Algorithm Toolbox User\325s Guide) 63.65 61.61 T
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