遗传算法组件的C++实现_C++_代码_下载

GAlib examples mbw 5jan96 How to compile the examples ------------------------------------------------------------------------------- Copy the contents of this directory into your own filespace. In the new directory, edit the makefile as needed to specify the location of the GAlib headers and library. If you system supports makedepend, do 'make depend' to update the makefile dependencies. To build all of the examples, type 'make all' or simply 'make'. To build a specific example, type 'make exN' where N is the number of the example you want to build. For example, to build example 5 you would type 'make ex5'. To remove old core, executable, object, and data files, do 'make clean'. You only need to do 'make depend' when the dependencies change. For example, if you modify one of the examples so that it uses routines in another file, you will have to modify the makefile then do 'make depend' so that make will know about the new dependency. What the examples do ------------------------------------------------------------------------------- ex1 Fill a 2DBinaryStringChromosome with alternating 0s and 1s using a SimpleGA. ex2 Generate a sequence of random numbers, then use a Bin2DecChromosome and SimpleGA to try and match the sequence. This example shows how to use the user-data member of genomes in objective functions. ex3 Read a 2D pattern from a data file then try to match the pattern using a 2DBinaryStringChromosome and a SimpleGA. This example also shows how to use the GAParametes object for setting genetic algorithm parameters and reading command-line arguments. ex4 Fill a 3DBinaryStringChromosome with alternating 0s and 1s using a SteadyStateGA. This example uses many member functions of the genetic algorithm to control which statistics are recorded and dumped to file. ex5 This example shows how to build a composite genome (a cell?) using a 2DBinaryStringGenome and a Bin2DecGenome. The composite genome uses behaviors that are defined in each of the genomes that it contains. The objective is to match a pattern and sequence of numbers. ex6 Grow a GATreeGenome<int> using a SteadyStateGA. This example illustrates the use of specialized methods to override the default initialization method and to specialize the output from a tree. It also shows how to use templatized genome classes. Finally, it shows the use of the parameters object to set default values then allow these to be modified from the command line. The objective function in this example tries to grow the tree as large as possible. ex7 Identical in function to example 3, this example shows how to use the increment operator (++), completion measure, and other member functions of the GA. It uses a GA with overlapping populations rather than the non-overlapping GA in example 3 and illustrates the use of many of the GA member functions. It also illustrates the use of the parameter list for reading settings from a file, and shows how to stuff a genome with data from an input stream. ex8 Grow a GAListGenome<int> using a GA with overlapping populations. This shows how to randomly initialize a list of integers, how to use the sigma truncation scaling object to handle objective scores that may be positive or negative, and the 'set' member of the genetic algorithm for controlling statistics and other genetic algorithm parameters. ex9 Find the maximum value of a continuous function in two variables. This example uses a GABin2DecGenome and simple GA. It also illustrates how to use the GASigmaTruncationScaling object (rather than the default linear scaling). Sigma truncation is particularly useful for objective functions that return negative values. ex10 Find the maximum value of a continuous, periodic function. This example illustrates the use of sharing to do speciation. It defines a sample distance function (one that does the distance measure based on the genotype, the other based on phenotype). It uses a binary- to-decimal genome to represent the function values. ex11 Generate a sequence of descending numbers using an order-based list. This example illustrates the use of a GAListGenome as an order-based chromosome. It contains a custom initializer and shows how to use this custom initializer in the List genome. ex12 Alphabetize a sequence of characters. Similar to example 11, this example illustrates the use of the GAStringGenome (rather than a list) as an order-based chromosome. ex13 This program runs a GA-within-GA. The outer level GA tries to match the pattern read in from a file. The inner GA tries to match a sequence of randomly generated numbers (the sequence is generated at the beginning of the program's execution). The inner level GA is run only when the outer GA reaches a threshhold objective score. ex14 Another illustration of how to use composite chromosomes. In this example, the composite chromosome contains a user-specifiable number of lists. Each list behaves differently and is not affected by mutations, crossovers, or initializations of the other lists. ex15 The completion function of a GA determines when it is "done". This example uses the convergence to tell when the GA has reached the optimum (the default completion measure is number-of-generations). It uses a binary-to-decimal genome and tries to match a sequence of randomly generated numbers. ex16 Tree chromosomes can contain any kind of object in the nodes. This example shows how to put a point object into the nodes of a tree to represent a 3D plant. The objective function tries to maximize the size of the plant. ex17 Array chromsomes can be used when you need tri-valued alleles. This example uses a 2D array with trinary alleles. ex18 This example compares the performance of three different genetic algorithms. The genome and objective function are those used in example 3, but this example lets you specify which type of GA you want to use to solve the problem. You can use steady state, simple, or incremental just by specifying one of them on the command line. The example saves the generational data to file so that you can then plot the convergence data to see how the performance of each genetic algorithm compares to the others. ex19 The 5 DeJong test problems. ex20 Holland's royal road function. This example computes Holland's 1993 ICGA version of the Royal Road problem. Holland posed this problem as a challenge to test the performance of genetic algorithms and challenged other GA users to match or beat his performance. ex21 This example illustrates various uses of the allele set in array genomes. The allele set may be an enumerated list of items or a bounded range of continuous values, or a bounded set of discrete values. This example shows how each of these may be used in combination with a real number genome. ex22 This example shows how to derive a new genetic algorithm class in order to customize the replacement method. Here we derive a new type of steady-state genetic algorithm in which speciation is done more effectively by not only scaling fitness values but also by controlling the way new individuals are inserted into the population. ex23 The genetic algorithm object can either maximize or minimize your objective function. This example shows how to use the minimize abilities of the genetic algorithm. It uses a real number genome with one element to find the maximum or minimum of a sinusoid. ex24 This example shows how to restricted mating using a custom genetic algorithm and custom selection scheme. The restricted mating in the genetic algorithm tries to pick individuals that are similar (based upon their comparator). The selector chooses only the upper half of the popul