GENETIC PROGRAMMING A SEMINAR REPORT
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GENETIC PROGRAMMINGA SEMINAR REPORT
COMPUTER SCIENCE & ENGINEERING
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Genetic programming (GP) is an automated methodology inspired by biological evolution to find computer programs that best perform a user- defined task. It is therefore a particular machine learning technique that uses an evolutionary algorithm to optimize a population of computer programs according to a fitness landscape determined by a program's ability to perform a given computational task. Computer programs in GP can be written in a variety of programming languages. In the early implementations of GP, program instructions and data values were organized in tree-structures, thus favoring the use of languages that naturally embody such a structure. Other forms of GP have been suggested and successfully implemented, such as the simpler linear representation which suits the more traditional imperative languages .GP is very computationally intensive and so in the 1990s it was mainly used to solve relatively simple problems. However, more recently, thanks to various improvements in GP technology and to the well known exponential growth in CPU power, GP has started delivering a number of outstanding results. Now about 40 human-competitive results have been gathered, in 5 areas such as quantum computing, electronic design, game playing, sorting, searching and many more. These results include the replication or infringement of several post-year-2000 inventions, and the production of two patentable new inventions. Developing a theory for GP has been very difficult and so in the 1990s genetic programming was considered a sort of pariah amongst the various techniques of search. However, after a series of breakthroughs in the early 2000s, the theory of GP has had a formidable and rapid development. So much so that it has been possible to build exact probabilistic models of GP and to show that GP is more general than, and in fact includes, genetic algorithms. Genetic Programming techniques have now been applied to evolvable hardware as well as computer programs. Meta-Genetic Programming is the technique of evolving a genetic programming system using genetic programming itself. Critics have argued that it is theoretically impossible, but more research is needed.
Genetic programming (GP) is an evolutionary computation technique that automatically solves problems without requiring the user to know or specify the form or structure of the solution in advance. At the most abstract level GP is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Since its inception, GP has attracted the interest of myriads of people around the globe. Things continue to change rapidly in genetic programming as investigators and practitioners discover new methods and applications. Genetic programming (GP) is an evolutionary computation technique
1.1 GENETIC PROGRAMMING IN A NUTSHELL
In genetic programming we evolve a population of computer programs. That is, generation by generation, GP stochastically transforms populations of programs into new, hopefully better, populations of programs. GP, like nature, is a random process, and it can never guarantee results.GPâ„¢s essential randomness, however, can lead it to escape traps which deterministic methods may be captured by. Like nature, GP has been very successful at evolving novel and unexpected ways of solving problems. The basic control flow for genetic programming, where survival of the fittest is used to find solutions. The basic control flow for genetic programming, where survival of the fittest is used to find solutions.
The basic steps in a GP system are shown in Algorithm below:
1: Randomly create an initial population of programs from the available primitives
3: Execute each program and ascertain its fitness.
4: Select one or two program(s) from the population with a probability based on fitness to participate in genetic operations. Create new individual program(s) by applying genetic operations with specified probabilities
6: until an acceptable solution is found or some other stopping condition is met (e.g., a maximum number of generations is reached).
7: return the best-so-far individual. How well a program works by running it, and then comparing its behavior to some ideal . We might be interested, for example, in how well a program predicts a time series or controls an industrial process. This comparison is quantified to give a numeric value called fitness. Those programs that do well are chosen to breed and produce new programs for the next generation. The primary genetic operations that are used to create new programs from existing ones are: Â¢ Crossover: The creation of a child program by combining randomly chosen parts from two selected parent programs. Â¢ Mutation: The creation of a new child program by randomly altering a randomly chosen part of a selected parent program.
1.2 WHY GENETIC PROGRAMMING??
Genetic programming is a machine learning model which, its adherents would claim, is the most general and flexible around. It saves time by freeing the human from having to design complex algorithms. Not only designing the algorithms but creating ones that give optimal solutions. It is again a type of artificial intelligence. It has already been applied to a wide variety of problem domains and may well have real-world utility.
1.3 HISTORY OF GP
The first results on the GP methodology were reported by Stephen F. Smith (1980) and Nichael L. Cramer (1985). In 1981 Forsyth reported the evolution of small programs in forensic science for the UK police. John R. Koza is a main proponent of GP and has pioneered the application of genetic programming in various complex optimization and search problems.
GP is very computationally intensive and so in the 1990s it was mainly used to solve relatively simple problems. But more recently, thanks to improvements in GP technology and to the exponential growth in CPU power, GP produced many novel and outstanding results in areas such as quantum computing, electronic design, game playing, sorting, searching and many more. These results include the replication or development of several post-year-2000 inventions. GP has also been applied to evolvable hardware as well as computer programs. There are several GP patents listed in the web site . Developing a theory for GP has been very difficult and so in the 1990s GP was considered a sort of outcast among search techniques. But after a series of breakthroughs in the early 2000s, the theory of GP has had a formidable and rapid development. So much so that it has been possible to build exact probabilistic models of GP (schema theories and Markov chain models).
In GP, programs are usually expressed as syntax trees rather than as lines of code. Figure below shows the tree representation of the program max(x+x,x+3*y). The variables and constants in the program (x, y and 3) are leaves of the tree. In GP they are called terminals, whilst the arithmetic operations (+, * and max) are internal nodes called functions. The sets of allowed functions and terminals together form the primitive set of a GP system
Languages that provide automatic garbage collection and dynamic lists as fundamental data types make it easier to implement expression In more advanced forms of GP, programs can be composed of multiple components (e.g., subroutines). In this case the representation used in GP is a set of trees (one for each component) grouped together under a special root node that acts as glue. We will call these (sub) trees branches. The number and type of the branches in a program, together with certain other features of their structure, form the architecture of the program. It is common in the GP literature to represent expressions in a prefix notation similar to that used in Lisp or Scheme. For example, max(x+x,x+3*y) becomes (max (+ x x) (+ x (* 3 y))). This notation often makes it easier to see the relationship between (sub) expressions and their corresponding (sub)trees. .
How one implements GP trees will obviously depend a great deal on the programming languages and libraries being used. trees and the necessary GP operations. Most traditional languages used in AI research (e.g., Lisp and Prolog), many recent languages (e.g., Ruby and Python), and the languages associated with several scientific programming tools (e.g., MATLAB and Mathematica) have these facilities. In other languages, one may have to implement lists/trees or use libraries that provide such data structures. In high performance environments, the tree-based representation of programs may be too inefficient since it requires the storage and management of numerous pointers. In some cases, it may be desirable to use GP primitives which accept a variable number of arguments (a quantity we call arity). An example is the sequencing instruction progn, which accepts any number of arguments executes them one at a time and then returns the value returned by the last argument. However, fortunately, it is now extremely common in GP applications for all functions to have a fixed number of arguments. If this is the case, then, the brackets in prefix-notation expressions are redundant, and trees can efficiently be represented as simple linear sequences. In effect, the functionâ„¢s name gives its arity and from the arities the brackets can be inferred. For example, the expression (max (+ x x) (+ x (* 3 y))) could be written unambiguously as the sequence max + x x + x * 3 y. The choice of whether to use such a linear representation or an explicit tree representation is typically guided by questions of convenience, efficiency, the genetic operations being used (some may be more easily or more efficiently implemented in one representation), and other data one may wish to collect during runs. (It is sometimes useful to attach additional information to nodes, which may be easier to implement if they are explicitly represented). These tree representations are the most common in GP, e.g., numerous high-quality, freely available GP implementations use them.
Non-tree representations have been suggested and successfully implemented, such as the simpler linear genetic programming which suits the more traditional imperative languages. The commercial GP software Discipulus, uses AIM, automatic induction of binary machine code to achieve better performance. MicroGP , uses a representation similar to linear GP to generate programs that fully exploit the syntax of a given assembly language.
3. GENETIC PRINCIPLES
The main operators used in evolutionary algorithms such as GP are crossover and mutation.
Mutation affects an individual in the population. It can replace a whole node in the selected individual, or it can replace just the node's information. To maintain integrity, operations must be fail-safe or the type of information the node holds must be taken into account. For example, mutation must be aware of binary operation nodes, or the operator must be able to handle missing values.
A simple piece of code:
Individual. Information = random Information;
Individual = generateNewIndividual;
3.2 Is Mutation Necessary?
Mutation was used in early experiments in the evolution of programs. It was not, however, used in (Koza, 1992) and (Koza, 1994), as Koza wished to demonstrate that mutation was not necessary and that GP was not performinga simple random search. This has significantly influenced the field, and mutation is often omitted from GP runs. While mutation is not necessary for GP to solve many problems, Oâ„¢Reilly (1995) argued that mutation â€ in combination with simulated annealing or stochastic iterated hill climbing â€ can perform as well as crossover-based GP in some cases. Nowadays, mutation is widely used in GP, especially in modeling applications. Koza also advises to use of a low level of mutation. Comparisons of crossover and mutation suggest that including mutation can be advantageous. Chellapilla found that a combination of six mutation operators performed better than previously published GP work on four simple problems.
Harries and Smith (1997) also found that mutation based hill climbers outperformed crossover-based GP systems on similar problems. Luke and Spector (1997) suggested that the situation is complex, and that the relative performance of crossover and mutation depends on both the problem and the details of the GP system.
3.3 GP Crossover
allow for the two parents having different shapes, one-point crossover analyses During biological sexual reproduction, the genetic material from both mother and father is combined in such a way that genes in the child are in approximately the same position as they were in its parents. This is quite different from traditional tree- based GP crossover, which can move a sub tree to a totally different position in the tree structure.
Crossover operators that tend to preserve the position of genetic material are called homologous, and several notions of homologous crossover have been proposed for GP. It is fairly straightforward to realize homologous crossover when using linear representations and homologous operators are widely used in linear GP. Various forms of homologous crossover have also been proposed for tree-based GP. The oldest homologous crossover in tree-based GP is one-point crossover. This works by selecting a common crossover point in the parent programs and then swapping the corresponding point only from the parts sub trees. To the two trees from the root nodes and selects the crossover of the two trees in the common region . In the common region, the parents have the same shape. The common region is related to homology, in the sense that the common region represents the result of a matching process between parent trees. Within the common region between two parent trees, the transfer of homologous primitives can happen like it does in a linear bit string genetic algorithm. Uniform crossover for trees works (in the common region) like uniform crossover in GAs. That is, the offspring are created by visiting the nodes in the common region and flipping a coin at each locus to decide whether the corresponding offspring node should be picked from the first or the second parent. If a node to be inherited belongs to the base of the common region and is a function, then the sub tree rooted there is inherited as well. With this form of root than with other operators. In context-preserving crossover, the crossover points are constrained to have the same coordinates, like crossover, there can be a greater mixing of the code near the in one-point crossover. Note that the crossover points are not limited to the common region. In size-fair crossover the first crossover point is selected randomly, as with standard crossover.
Then the size of the sub tree to be removed from the first parent is calculated. This is used to constrain the choice of the second crossover point so as to guarantee that the sub tree excised from the second parent will not be unfairly big. Harries and Smith (1997) suggested five new crossover operators that are like standard crossover but with probabilistic restrictions on the depth of crossover points within the parent trees. Since crossover and mutation are specific to the representation used in GP, each new representation tends to need new crossover and mutation operators. For example ripple crossover is a way of looking at crossover in grammatical evolution
3.4 Fitness function
Each individual in a population is assigned a fitness value as a result of its interaction with the environment. Fitness is the driving force of Darwinian natural selection and, likewise, of genetic algorithms. The environment is a set of cases which provides a basis for evaluating the fitness of the S-expressions in the population For most of the problems described herein, the Ãƒâ€™raw fitnessÃƒâ€œ of any LISP S-expression is the sum of the distances (taken over all the environmental cases) between the point in the range space returned by the S-expression for a given set of arguments and the correct point in the range space. The S-expression may Boolean-valued, integer-valued, real-valued, complex-valued, vector-valued, multiple-valued, or symbolic-valued. If the S-expression is integer-valued or real-valued, the sum of distances is the sum of absolute values of the differences between the numbers involved. In particular, the raw fitness r(i,t) of an individual LISP S-expression i in the population of size M at any generational time step t is where S(i,j) is the value returned by S-expression i for environmental case j (of Ne environmental cases) and where C(j) is the correct value for environmental case j .If the S-expression is Boolean-valued or symbolic-valued, the sum of distances is equivalent to the number of mismatches. If the S-expression is complex-valued, or vector-valued, or multiple-valued, the sum of the distances is the sum of the distances separately obtained from each component of the vector or list. The closer this sum of distances is to zero, the better the S-expression. If the S-expression (or each component of a vector or list) is real-valued or integer-valued, the square root of the sum of squares of distances can, alternatively, be used to measure fitness (thereby increasing the influence of more distant points). For some problems described herein, the fitness function does not work with the actual value returned by the individual S-expression, but some number (e.g. elapsed time, total score, cases handled, etc.) which is indirectly created by the action of the S-expression. Nonetheless, the raw fitness function must be defined in such a way that the raw fitness is to be smaller for better individuals in the population. For each problem described herein, the raw fitness r(i,t) is then adjusted (scaled) to produce an adjusted fitness measure a(i,t). The Ãƒâ€™adjusted fitnessÃƒâ€œ value a(i,t) is where r(i,t) is the raw fitness for individual i at time t. Unlike raw fitness, the adjusted fitness is larger for better individuals in the population. Moreover, the adjusted fitness lies between 0 and 1. Each such adjusted fitness value a(i,t) is then normalized. The Ãƒâ€™normalized fitnessÃƒâ€œ value n(i,t) is The normalized fitness not only ranges between 0 and 1 and is larger for better individuals in the population, but the sum of the normalized fitness values is 1. When we use the phrases "proportional to fitness" or "fitness proportionate" in this paper, we are referring to normalized fitness. As will be seen, it is also possible for the fitness function to consider secondary factors (e.g., efficiency of the S-expression, parsimony of the S-expression, compliance with the initial conditions of a differential equation, etc.). When the number of environmental cases is large, it is sometimes expeditious to compute the fitness function using only a sampling of the possible environmental cases (including, possibly, a sampling that varies from generation to generation to minimize the possible bias resulting from such sampling
4. HOW GENETIC PROGRAMMING WORKS??
Genetic programming starts with a primordial ooze of thousands of randomly created computer programs. This population of programs is progressively evolved over a series of generations. The evolutionary search uses the Darwinian principle of natural selection (survival of the fittest) and analogs of various naturally occurring operations, including crossover (sexual recombination), mutation, gene duplication, gene deletion. Genetic programming sometimes also employs developmental processes by which an embryo grows into fully developed organism. Old Chinese saying says animated gif is worth one mega-word). In addition, genetic programming can automatically create, in a single run, a general (parameterized) solution to a problem in the form of a graphical structure whose nodes or edges represent components and where the parameter values of the components are specified by mathematical expressions containing free variables. That is, genetic programming can automatically create a general solution to a problem in the form of a parameterized topology.
4.1 PREPARATORY STEPS OF GP
Genetic programming starts from a high-level statement of the requirements of a problem and attempts to produce a computer program that solves the problem. The human user communicates the high-level statement of the problem to the genetic programming system by performing certain well-defined preparatory steps. The five major preparatory steps for the basic version of genetic programming require the human user to specify
(1) The set of terminals (e.g., the independent variables of the problem, zero-argument functions, and random constants) for each branch of the to-be-evolved program,
(2) The set of primitive functions for each branch of the to-be-evolved program,
(3) The fitness measure (for explicitly or implicitly measuring the fitness of individuals in the population),
(4) Certain parameters for controlling the run, and
(5) The termination criterion and method for designating the result of the run.
The figure below shows the five major preparatory steps for the basic version of genetic programming. The preparatory steps (shown at the top of the figure) are the human- supplied input to the genetic programming system. The computer program (shown at the bottom) is the output of the genetic programming system.
4.1 Preparatory steps of the first two preparatory steps specify the ingredients that are available to create the Computer programs. A run of genetic programming is a competitive search among a diverse population of programs composed of the available functions and terminals. Function Set and Terminal Set The identification of the function set and terminal set for a particular problem (or category of problems) is usually a straightforward process. For some problems, the function set may consist of merely the arithmetic functions of addition, subtraction, multiplication, and division as well as a conditional branching operator. The terminal set may consist of the programâ„¢s external inputs (independent variables) and numerical constants. This function set and terminal set is useful for a wide variety of problems (and corresponds to the basic operations found in virtually every general-purpose digital computer).
For many other problems, the ingredients include specialized functions and terminals. For example, if the goal is to get genetic programming to automatically program a robot to mop the entire floor of an obstacle-laden room, the human user must tell genetic programming what the robot is capable of doing. For example, the robot may be capable of executing functions such as moving, turning, and swishing the mop. If the goal is the automatic creation of a controller, the function set may consist of signal- processing functions that operates on time-domain signals, including integrators, differentiators, leads, lags, gains, adders, sub tractors, and the like. The terminal set may consist of signals such as the reference signal and plant output. Once the human user has identified the primitive ingredients for a problem of controller synthesis, the same function set and terminal set can be used to automatically synthesize a wide variety of different controllers. If a complex structure, such an antenna, is to be designed, it may be desirable to use functions that cause a turtle to draw the complex structure. If the goal is the automatic synthesis of an analog electrical circuit, the function set may enable genetic programming to construct circuits from components such as transistors, capacitors, and resistors. This construction (developmental) process typically starts with a very simple embryonic structure, such as a single modifiable wire. If, additionally, such a function set is geographically aware, a circuitâ„¢s placement and routing can be synthesized at the same as its topology and sizing. Once the human user has identified the primitive ingredients for a problem of circuit synthesis, the same function set and terminal set can be used to automatically synthesize an amplifier, computational circuit, active filter, voltage reference circuit, or any other circuit composed of these ingredients.
The third preparatory step concerns the fitness measure for the problem. The fitness measure specifies what needs to be done. The fitness measure is the primary mechanism for communicating the high-level statement of the problemâ„¢s requirements to the genetic programming system. For example, if the goal is to get genetic programming to automatically synthesize an amplifier, the fitness function is the mechanism for telling genetic programming to synthesize a circuit that amplifies an incoming signal (as opposed to, say, a circuit that suppresses the low frequencies of an incoming signal or a circuit that computes the square root of the incoming signal). The first two preparatory steps define the search space whereas the fitness measure implicitly specifies the searchâ„¢s desired goal.
The fourth and fifth preparatory steps are administrative. The fourth preparatory step entails specifying the control parameters for the run. The most important control parameter is the population size. In practice, the user may choose a population size that will produce a reasonably large number of generations in the amount of computer time we are willing to devote to a problem (as opposed to, say, analytically choosing the population size by somehow analyzing a problemâ„¢s fitness landscape). Other control parameters include the probabilities of performing the genetic operations, the maximum size for programs, and other details of the run.
The fifth preparatory step consists of specifying the termination criterion and the method of designating the result of the run. The termination criterion may include a maximum number of generations to be run as well as a problem-specific success predicate. In practice, one may manually monitor and manually terminate the run when the values of fitness for numerous successive best-of-generation individuals appear to have reached a plateau. The single best-so-far individual is then harvested and designated as the result of the run.
Running After the human user has performed the preparatory steps for a problem, the run of genetic programming can be launched. Once the run is launched, a series of well-defined, problem-independent executional step is executed.
4.2 EXECUTIONAL STEPS OF GENETIC
Genetic programming typically starts with a population of randomly generated computer programs composed of the available programmatic ingredients. Iteratively transforms a population of computer programs into a new generation of the population by applying analogs of naturally occurring genetic operations. These operations are applied to individual(s) selected from the population. The individuals are probabilistically selected to participate in the genetic operations based on their fitness (as measured by the fitness measure provided by the human user in the third preparatory step). The iterative transformation of the population is executed inside the main generational loop of the run of genetic programming.
The executional steps of genetic programming are as follows:
(1) Randomly create an initial population (generation 0) of individual computer programs composed of the available functions and terminals.
(2) Iteratively perform the following sub-steps (called a generation) on the population until the termination criterion is satisfied:
(a) Execute each program in the population and ascertain its fitness (explicitly or implicitly) using the problemâ„¢s fitness measure.
(b) Select one or two individual program(s) from the population with a probability based on fitness (with reselection allowed) to participate in the genetic operations in ©.
© Create new individual program(s) for the population by applying the following genetic operations with specified probabilities:
(i) Reproduction: Copy the selected individual program to the new population.
(ii) Crossover: Create new offspring program(s) for the new population by recombining randomly chosen parts from two selected programs.
(iii) Mutation: Create one new offspring program for the new population by randomly mutating a randomly chosen part of one selected program.
(iv) Architecture-altering operations: Choose an architecture-altering operation from the available repertoire of such operations and create one new offspring program for the new population by applying the chosen architecture-altering operation to one selected program
(3 ) After the termination criterion is satisfied, the single best program in the population produced during the run (the best-so-far individual) is harvested and designated as the result of the run. If the run is successful, the result may be a solution (or approximate solution) to the problem.
4.3 EXAMPLES OF GP
Â¢ Symbolic regression - is the process of discovering both the functional form of a target function and all of its necessary coefficients, or at least an approximation to these. This is distinct from other forms of regression such as polynomial regression in which you are merely trying to find the coefficients of a polynomial of a pre specified order. In some senses, all GP problems are symbolic regression problems, but the term is usually used to apply to finding real-valued functions. Â¢ Analog circuit design-Embryo circuit-initial circuit which is modified to create a new circuit according to functionality criteria.
5. META GP
Meta-Genetic Programming is the proposed Meta learning technique of evolving a genetic programming system using genetic programming itself. It suggests that chromosomes, crossover, and mutation were themselves evolved, therefore like their real life counterparts should be allowed to change on their own rather than being determined by a human programmer. Meta-GP was proposed by JÃƒÂ¼rgen Schmidhuber in 1987; it is a recursive but terminating algorithm, allowing it to avoid infinite recursion. Critics of this idea often say this approach is overly broad in scope. However, it might be possible to constrain the fitness criterion onto a general class of results, and so obtain an evolved GP that would more efficiently produce results for sub-classes. This might take the form of a Meta evolved GP for producing human walking algorithms which is then used to evolve human running, jumping, etc. The fitness criterion applied to the Meta GP would simply be one of efficiency. For general problem classes there may be no way to show that Meta GP will reliably produce results more efficiently than a created algorithm other than exhaustion. The same holds for standard GP and other search algorithms
6. APPLICATIONS OF GP
There are numerous applications of genetic programming. Some of them are: Â¢ Black Art Problems, such as the automated synthesis of analog electrical circuits, controllers, antennas, networks of chemical reactions, optical systems, and other areas of design , Â¢ Programming The Unprogrammable (PTU) involving the automatic creation of computer programs for unconventional computing devices such as cellular automata, multi-agent systems, parallel programming systems, field-programmable gate arrays, field-programmable analog arrays, ant colonies, swarm intelligence, distributed systems, and the like. Â¢ Commercially Useful New Inventions (CUNI) involving the use of genetic programming as an automated "invention machine" for creating commercially usable new inventions
Genetic programming is limited by the following factors:
1. Conventional programs are extremely fragile with respect to random changes. Possible direction: Use less fragile representation
2. Fitness function hard to define (binary nature of many solutions; how bad is a wrong answer?) Possible
Concentrate on application domains where fitness is easy to define
Today, we can see that indeed GP has started fulfilling dreams by providing us with a systematic method, based on Darwinian evolution, for getting computers to automatically solve hard real-life problems. To do so, it simply requires a high-level statement of what needs to be done and enough computing power. At present GP is unable to produce computer programs that would pass the full Turing test for machine intelligence, and it might not be ready for this immense task for centuries. Nonetheless, thanks to the constant improvements in GP technology, in its theoretical foundations and in computing power, GP has been able to solve dozens of difficult problems with human-competitive results and to provide valuable solutions to many other problems. It is reasonable to predict that in a few years time GP will be able to routinely and competently solve important problems for us, in a variety of application domains with human-competitive performance. Genetic programming will then become an essential collaborator for many human activities. This will be a remarkable step forward towards achieving true human-competitive machine intelligence.
Genetic Programming: On the Programming of Computers by Means of Natural selection
By: John R. Koza
A Field Guide to By :Riccardo Poli
William B. Langdon
Nicholas F. McPhee
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A Field Guide toGenetic Programming
The goal of having computers automatically solve problems is central toartificial intelligence, machine learning, and the broad area encompassed bywhat Turing called “machine intelligence” (Turing, 1948). Machine learningpioneer Arthur Samuel, in his 1983 talk entitled “AI: Where It Has Beenand Where It Is Going” (Samuel, 1983), stated that the main goal of thefields of machine learning and artificial intelligence is:“to get machines to exhibit behaviour, which if done by humans,would be assumed to involve the use of intelligence.”Genetic programming (GP) is an evolutionary computation (EC)1 techniquethat automatically solves problems without requiring the user to knowor specify the form or structure of the solution in advance. At the mostabstract level GP is a systematic, domain-independent method for gettingcomputers to solve problems automatically starting from a high-level statementof what needs to be done.Since its inception, GP has attracted the interest of myriads of peoplearound the globe. This book gives an overview of the basics of GP, summarisedimportant work that gave direction and impetus to the field anddiscusses some interesting new directions and applications. Things continueto change rapidly in genetic programming as investigators and practitionersdiscover new methods and applications. This makes it impossible to coverall aspects of GP, and this book should be seen as a snapshot of a particularmoment in the history of the field.
1.1 Genetic Programming in a Nutshell
In genetic programming we evolve a population of computer programs. Thatis, generation by generation, GP stochastically transforms populations ofprograms into new, hopefully better, populations of programs, cf. Figure 1.1.GP, like nature, is a random process, and it can never guarantee results.GP’s essential randomness, however, can lead it to escape traps which deterministicmethods may be captured by. Like nature, GP has been verysuccessful at evolving novel and unexpected ways of solving problems. (SeeChapter 12 for numerous examples.)The basic steps in a GP system are shown in Algorithm 1.1. GP finds outhow well a program works by running it, and then comparing its behaviourto some ideal (line 3). We might be interested, for example, in how well aprogram predicts a time series or controls an industrial process. This comparisonis quantified to give a numeric value called fitness. Those programsthat do well are chosen to breed (line 4) and produce new programs for thenext generation (line 5). The primary genetic operations that are used tocreate new programs from existing ones are:• Crossover: The creation of a child program by combining randomlychosen parts from two selected parent programs.• Mutation: The creation of a new child program by randomly alteringa randomly chosen part of a selected parent program.
1.2 Getting Started
Two key questions for those first exploring GP are:1. What should I read to get started in GP?2. Should I implement my own GP system or should I use an existingpackage? If so, what package should I use?1.3 Prerequisites 31: Randomly create an initial population of programs from the availableprimitives (more on this in Section 2.2).2: repeat3: Execute each program and ascertain its fitness.4: Select one or two program(s) from the population with a probabilitybased on fitness to participate in genetic operations (Section 2.3).5: Create new individual program(s) by applying genetic operations withspecified probabilities (Section 2.4).6: until an acceptable solution is found or some other stopping conditionis met (e.g., a maximum number of generations is reached).7: return the best-so-far individual.Algorithm 1.1: Genetic ProgrammingThe best way to begin is obviously by reading this book, so you’re off toa good start. We included a wide variety of references to help guide peoplethrough at least some of the literature. No single work, however, could claimto be completely comprehensive. Thus Appendix A reviews a whole host ofbooks, videos, journals, conferences, and on-line sources (including severalfreely available GP systems) that should be of assistance.We strongly encourage doing GP as well as reading about it; the dynamicsof evolutionary algorithms are complex, and the experience of tracingthrough runs is invaluable. In Appendix B we provide the full Javaimplementation of Riccardo’s TinyGP system.
Although this book has been written with beginners in mind, unavoidablywe had to make some assumptions about the typical background of ourreaders. The book assumes some working knowledge of computer scienceand computer programming; this is probably an essential prerequisite to getthe most from the book.We don’t expect that readers will have been exposed to other flavours ofevolutionary algorithms before, although a little background might be useful.The interested novice can easily find additional information on evolutionarycomputation thanks to the plethora of tutorials available on the Internet.Articles from Wikipedia and the genetic algorithm tutorial produced byWhitley (1994) should suffice.
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