cf gis readhexstring pop 
          cf bis readhexstring pop w gray}  image
        bitmapsave restore 
        grestore
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/BITMAPGRAY { 
	8 {fakecolorsetup} COMMONBITMAP
	} bind def
/BITMAPGRAYc { 
	8 {fakecolorsetup} COMMONBITMAPc
	} bind def
/ENDBITMAP {
	} bind def
end 
	/ALDsave FMLOCAL
	/ALDmatrix matrix def ALDmatrix currentmatrix pop
/StartALD {
	/ALDsave save def
	 savematrix
	 ALDmatrix setmatrix
	} bind def
/InALD {
	 restorematrix
	} bind def
/DoneALD {
	 ALDsave restore
	} bind def
%%EndProlog
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595.3 841.9 0 FMBEGINPAGE
0 10 Q
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(Genetic Algorithm Toolbox User\325s Guide) 63.65 61.61 T
(1-1) 518.33 61.29 T
1 28 Q
(1 T) 63.65 726.29 T
(utorial) 100.75 726.29 T
2 12 Q
4.82 (M) 135.65 692.95 P
2 10 Q
4.02 (A) 146.31 692.95 P
4.02 (TLAB) 152.42 692.95 P
2 12 Q
4.82 ( has a wide variety of functions useful to the genetic algorithm) 178.51 692.95 P
1.63 (practitioner and those wishing to experiment with the genetic algorithm for the) 135.65 678.95 P
-0.03 (\336rst time. Given the versatility of M) 135.65 664.95 P
2 10 Q
-0.02 (A) 309.05 664.95 P
-0.02 (TLAB) 315.16 664.95 P
2 12 Q
-0.03 (\325) 341.26 664.95 P
-0.03 (s high-level language, problems can be) 344.59 664.95 P
0.58 (coded in m-\336les in a fraction of the time that it would take to create C or Fortran) 135.65 650.95 P
3.7 (programs for the same purpose. Couple this with M) 135.65 636.95 P
2 10 Q
3.09 (A) 412.78 636.95 P
3.09 (TLAB) 418.88 636.95 P
2 12 Q
3.7 (\325) 444.98 636.95 P
3.7 (s advanced data) 448.31 636.95 P
0.17 (analysis, visualisation tools and special purpose application domain toolboxes and) 135.65 622.95 P
2.91 (the user is presented with a uniform environment with which to explore the) 135.65 608.95 P
(potential of genetic algorithms.) 135.65 594.95 T
1.02 (The Genetic Algorithm T) 135.65 568.95 P
1.02 (oolbox uses M) 260.1 568.95 P
2 10 Q
0.85 (A) 332.76 568.95 P
0.85 (TLAB) 338.87 568.95 P
2 12 Q
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
1.04 (Genetic Algorithm T) 135.65 540.95 P
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
FMENDPAGE
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595.3 841.9 0 FMBEGINPAGE
0 10 Q
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(Genetic Algorithm Toolbox User\325s Guide) 63.65 61.61 T
(1-2) 518.33 61.29 T
63.65 716.95 531.65 726.95 C
63.65 725.95 531.65 725.95 2 L
1 H
2 Z
0 X
0 K
N
-8.35 24.95 603.65 816.95 C
1 18 Q
0 X
0 K
(Installation) 63.65 732.95 T
2 12 Q
2.37 (Instructions for installing the Genetic Algorithm T) 135.65 694.95 P
2.37 (oolbox can be found in the) 391.55 694.95 P
0.36 (M) 135.65 680.95 P
2 10 Q
0.3 (A) 146.31 680.95 P
0.3 (TLAB) 152.42 680.95 P
2 12 Q
0.36 ( installation instructions. It is recommended that the \336les for this toolbox) 178.51 680.95 P
(are stored in a directory named genetic of) 135.65 666.95 T
(f the main matlab/toolbox directory) 334.93 666.95 T
(.) 504.71 666.95 T
3.33 (A number of demonstrations are available. A single-population binary-coded) 135.65 640.95 P
-0.13 (genetic algorithm to solve a numerical optimization problem is implemented in the) 135.65 626.95 P
-0.25 (m-\336le) 135.65 612.95 P
3 F
-0.61 (sga.m) 167.04 612.95 P
2 F
-0.25 (. The demonstration m-\336le) 203.02 612.95 P
3 F
-0.61 (mpga.m) 332.93 612.95 P
2 F
-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
(demonstration m-\336les are discussed in detail in the) 135.65 584.95 T
0 F
(Examples) 382.16 584.95 T
2 F
( Section.) 428.79 584.95 T
1.06 (Additionally) 135.65 558.95 P
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
3 F
6.11 (test_fns) 323.66 544.95 P
2 F
2.55 (, from the Genetic Algorithm) 381.23 544.95 P
0.05 (T) 135.65 530.95 P
0.05 (oolbox functions. A brief description of these test functions is given at the end of) 142.14 530.95 P
0.93 (the) 135.65 516.95 P
0 F
0.93 (Examples) 154.23 516.95 P
2 F
0.93 ( Section. A further document describes the implementation and use) 200.86 516.95 P
(of these functions.) 135.65 502.95 T
FMENDPAGE
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595.3 841.9 0 FMBEGINPAGE
0 10 Q
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(Genetic Algorithm Toolbox User\325s Guide) 63.65 61.61 T
(1-3) 518.33 61.29 T
63.65 716.95 531.65 726.95 C
63.65 725.95 531.65 725.95 2 L
1 H
2 Z
0 X
0 K
N
-8.35 24.95 603.65 816.95 C
1 18 Q
0 X
0 K
(An Overview of Genetic Algorithms) 63.65 732.95 T
2 12 Q
-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
1 16 Q
(What ar) 135.65 652.29 T
(e Genetic Algorithms?) 192.66 652.29 T
2 12 Q
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
2.41 (the principle of survival of the \336ttest to produce \050hopefully\051 better and better) 135.65 596.95 P
0.86 (approximations to a solution. At each generation, a new set of approximations is) 135.65 582.95 P
0.15 (created by the process of selecting individuals according to their level of \336tness in) 135.65 568.95 P
1.29 (the problem domain and breeding them together using operators borrowed from) 135.65 554.95 P
0.63 (natural genetics. This process leads to the evolution of populations of individuals) 135.65 540.95 P
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
3.17 (Individuals, or current approximations, are encoded as strings,) 135.65 486.95 P
0 F
3.17 (chr) 463.14 486.95 P
3.17 (omosomes) 478.68 486.95 P
2 F
3.17 (,) 528.65 486.95 P
0.8 (composed over some alphabet\050s\051, so that the) 135.65 472.95 P
0 F
0.8 (genotypes) 357.07 472.95 P
2 F
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
0 F
3.66 (phenotypic) 374.11 458.95 P
2 F
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
0 F
2.13 (x) 281.76 416.95 P
0 10 Q
1.78 (1) 287.08 413.95 P
2 12 Q
2.13 ( and) 292.08 416.95 P
0 F
2.13 (x) 319.66 416.95 P
0 10 Q
1.78 (2) 324.98 413.95 P
2 12 Q
2.13 (, may be mapped onto the chromosome) 329.98 416.95 P
(structure in the following way:) 135.65 402.95 T
-0.3 (where) 135.65 299.98 P
0 F
-0.3 (x) 167.65 299.98 P
0 10 Q
-0.25 (1) 172.98 296.98 P
2 12 Q
-0.3 ( is encoded with 10 bits and) 177.97 299.98 P
0 F
-0.3 (x) 312.82 299.98 P
0 10 Q
-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
0 F
1.94 (\336tness) 356.04 161.98 P
2 F
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
63.65 96.95 531.65 744.95 C
135.65 321.98 531.65 398.95 C
146.65 328.95 520.65 391.95 C
146.65 328.95 520.65 391.95 R
7 X
0 K
V
3 12 Q
0 X
(1 0 1 1 0 1 0 0 1 1 0 1 0 1 1 1 0 1 0 1 0 0 1 0 1) 157.35 369.97 T
170.49 343.64 158.95 346.95 170.49 350.26 170.49 346.95 4 Y
V
282.42 350.26 293.95 346.95 282.42 343.64 282.42 346.95 4 Y
V
170.49 346.95 282.42 346.95 2 L
0.5 H
0 Z
N
297.08 382.95 297.08 337.95 2 L
2 Z
11 X
N
311.49 343.64 299.95 346.95 311.49 350.26 311.49 346.95 4 Y
0 X
V
495.42 350.26 506.95 346.95 495.42 343.64 495.42 346.95 4 Y
V
311.49 346.95 495.42 346.95 2 L
0 Z
N
0 F
(x) 221.29 353.75 T
0 10 Q
(1) 226.62 350.75 T
0 12 Q
(x) 398.29 353.75 T
0 10 Q
(2) 403.61 350.75 T
135.65 321.98 531.65 398.95 C
63.65 96.95 531.65 744.95 C
-8.35 24.95 603.65 816.95 C
FMENDPAGE
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595.3 841.9 0 FMBEGINPAGE
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(Genetic Algorithm Toolbox User\325s Guide) 63.65 61.61 T
(1-4) 518.33 61.29 T
2 12 Q
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
0.45 (During the reproduction phase, each individual is assigned a \336tness value derived) 135.65 696.95 P
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
5.22 (the population, with a probability according to their relative \336tness, and) 135.65 586.95 P
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
3 F
(P) 171.65 450.95 T
3 10 Q
(1) 178.84 447.95 T
3 12 Q
( = 1 0 0 1 0 1 1 0) 184.84 450.95 T
2 F
(, and) 314.37 450.95 T
3 F
(P) 171.65 424.95 T
3 10 Q
(2) 178.84 421.95 T
3 12 Q
( = 1 0 1 1 1 0 0 0) 184.84 424.95 T
2 F
(.) 314.37 424.95 T
0.59 (If an integer position,) 135.65 398.95 P
0 F
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
0 F
0.97 (l) 172.6 384.95 P
2 F
0.97 (, minus one [1,) 175.93 384.95 P
0 F
0.97 (l) 254.42 384.95 P
2 F
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
0 F
(i = 5) 403.22 356.95 T
2 F
( is selected,) 426.64 356.95 T
3 F
(O) 171.65 330.95 T
3 10 Q
(1) 178.84 327.95 T
3 12 Q
( = 1 0 0 1 0 0 0 0) 184.84 330.95 T
2 F
(, and) 314.37 330.95 T
3 F
(O) 171.65 304.95 T
3 10 Q
(2) 178.84 301.95 T
3 12 Q
( = 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
0 F
1.8 (Pm) 354.88 236.95 P
2 F
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
4 F
0.74 (\336) 145.38 194.95 P
2 F
0.74 (1 or 1) 160.96 194.95 P
4 F
0.74 (\336) 194.15 194.95 P
2 F
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
2 12 Q
0.74 ( leads to the new) 448.09 194.95 P
(string,) 135.65 180.95 T