Newsgroups:,comp.answers,news.answers Path:!!!!!!!!!!!pipex!!!!!scmdb From: (David Beasley) Subject: FAQ: part 2/6 (A Guide to Frequently Asked Questions) Summary: This is part 2 of a entitled "The Hitch-Hiker's Guide to Evolutionary Computation". A periodically published list of Frequently Asked Questions (and their answers) about Evolutionary Algorithms, Life and Everything. It should be read by anyone who whishes to post to the newsgroup, preferably *before* posting. Originator: Sender: (Usenet News user) Message-ID: Supersedes: Approved: news-answers-request@MIT.Edu Date: Wed, 19 Mar 1997 19:21:55 GMT Expires: Sat, 21 Jun 1997 19:21:44 GMT X-Nntp-Posting-Host: References: Organization: Posted through the Joint Cardiff Computing Service, Wales, UK Followup-To: Lines: 1123 Xref: comp.answers:24948 news.answers:97544 Archive-name: ai-faq/genetic/part2 Last-Modified: 3/20/97 Issue: 5.1 TABLE OF CONTENTS OF PART 2 Q1: What are Evolutionary Algorithms (EAs)? Q1.1: What's a Genetic Algorithm (GA)? Q1.2: What's Evolutionary Programming (EP)? Q1.3: What's an Evolution Strategy (ES)? Q1.4: What's a Classifier System (CFS)? Q1.5: What's Genetic Programming (GP)? ---------------------------------------------------------------------- Subject: Q1: What are Evolutionary Algorithms (EAs)? Evolutionary algorithm is an umbrella term used to describe computer- based problem solving systems which use computational models of some of the known mechanisms EVOLUTION as key elements in their design and implementation. A variety of evolutionary algorithms have been proposed. The major ones are: GENETIC ALGORITHMs (see Q1.1), EVOLUTIONARY PROGRAMMING (see Q1.2), EVOLUTION STRATEGIEs (see Q1.3), CLASSIFIER SYSTEMs (see Q1.4), and GENETIC PROGRAMMING (see Q1.5). They all share a common conceptual base of simulating the evolution of INDIVIDUAL structures via processes of SELECTION, MUTATION, and REPRODUCTION. The processes depend on the perceived PERFORMANCE of the individual structures as defined by an ENVIRONMENT. More precisely, EAs maintain a POPULATION of structures, that evolve according to rules of selection and other operators, that are referred to as "search operators", (or GENETIC OPERATORs), such as RECOMBINATION and mutation. Each individual in the population receives a measure of it's FITNESS in the environment. Reproduction focuses attention on high fitness individuals, thus exploiting (cf. EXPLOITATION) the available fitness information. Recombination and mutation perturb those individuals, providing general heuristics for EXPLORATION. Although simplistic from a biologist's viewpoint, these algorithms are sufficiently complex to provide robust and powerful adaptive search mechanisms. --- "An Overview of Evolutionary Computation" [ECML93], 442-459. BIOLOGICAL BASIS To understand EAs, it is necessary to have some appreciation of the biological processes on which they are based. Firstly, we should note that EVOLUTION (in nature or anywhere else) is not a purposive or directed process. That is, there is no evidence to support the assertion that the goal of evolution is to produce Mankind. Indeed, the processes of nature seem to boil down to a haphazard GENERATION of biologically diverse organisms. Some of evolution is determined by natural SELECTION or different INDIVIDUALs competing for resources in the ENVIRONMENT. Some are better than others. Those that are better are more likely to survive and propagate their genetic material. In nature, we see that the encoding for genetic information (GENOME) is done in a way that admits asexual REPRODUCTION. Asexual reproduction typically results in OFFSPRING that are genetically identical to the PARENT. (Large numbers of organisms reproduce asexually; this includes most bacteria which some biologists hold to be the most successful SPECIES known.) Sexual reproduction allows some shuffing of CHROMOSOMEs, producing offspring that contain a combination of information from each parent. At the molecular level what occurs (wild oversimplification alert!) is that a pair of almost identical chromosomes bump into one another, exchange chunks of genetic information and drift apart. This is the RECOMBINATION operation, which is often referred to as CROSSOVER because of the way that biologists have observed strands of chromosomes crossing over during the exchange. Recombination happens in an environment where the selection of who gets to mate is largely a function of the FITNESS of the individual, i.e. how good the individual is at competing in its environment. Some "luck" (random effect) is usually involved too. Some EAs use a simple function of the fitness measure to select individuals (probabilistically) to undergo genetic operations such as crossover or asexual reproduction (the propagation of genetic material unaltered). This is fitness-proportionate selection. Other implementations use a model in which certain randomly selected individuals in a subgroup compete and the fittest is selected. This is called tournament selection and is the form of selection we see in nature when stags rut to vie for the privilege of mating with a herd of hinds. Much EA research has assumed that the two processes that most contribute to evolution are crossover and fitness based selection/reproduction. As it turns out, there are mathematical proofs that indicate that the process of fitness proportionate reproduction is, in fact, near optimal in some senses. Evolution, by definition, absolutely requires diversity in order to work. In nature, an important source of diversity is MUTATION. In an EA, a large amount of diversity is usually introduced at the start of the algorithm, by randomising the GENEs in the POPULATION. The importance of mutation, which introduces further diversity while the algorithm is running, therefore continues to be a matter of debate. Some refer to it as a background operator, simply replacing some of the original diversity which has been lost, while others view it as playing the dominant role in the evolutionary process. It cannot be stressed too strongly that an evolutionary algorithm (as a SIMULATION of a genetic process) is not a random search for a solution to a problem (highly fit individual). EAs use stochastic processes, but the result is distinctly non-random (better than random). PSEUDO CODE Algorithm EA is // start with an initial time t := 0; // initialize a usually random population of individuals initpopulation P (t); // evaluate fitness of all initial individuals in population evaluate P (t); // test for termination criterion (time, fitness, etc.) while not done do // increase the time counter t := t + 1; // select sub-population for offspring production P' := selectparents P (t); // recombine the "genes" of selected parents recombine P' (t); // perturb the mated population stochastically mutate P' (t); // evaluate it's new fitness evaluate P' (t); // select the survivors from actual fitness P := survive P,P' (t); od end EA. ------------------------------ Subject: Q1.1: What's a Genetic Algorithm (GA)? The GENETIC ALGORITHM is a model of machine learning which derives its behavior from a metaphor of some of the mechanisms of EVOLUTION in nature. This is done by the creation within a machine of a POPULATION of INDIVIDUALs represented by CHROMOSOMEs, in essence a set of character strings that are analogous to the base-4 chromosomes that we see in our own DNA. The individuals in the population then go through a process of simulated "evolution". Genetic algorithms are used for a number of different application areas. An example of this would be multidimensional OPTIMIZATION problems in which the character string of the chromosome can be used to encode the values for the different parameters being optimized. In practice, therefore, we can implement this genetic model of computation by having arrays of bits or characters to represent the chromosomes. Simple bit manipulation operations allow the implementation of CROSSOVER, MUTATION and other operations. Although a substantial amount of research has been performed on variable- length strings and other structures, the majority of work with genetic algorithms is focussed on fixed-length character strings. We should focus on both this aspect of fixed-lengthness and the need to encode the representation of the solution being sought as a character string, since these are crucial aspects that distinguish GENETIC PROGRAMMING, which does not have a fixed length representation and there is typically no encoding of the problem. When the genetic algorithm is implemented it is usually done in a manner that involves the following cycle: Evaluate the FITNESS of all of the individuals in the population. Create a new population by performing operations such as crossover, fitness-proportionate REPRODUCTION and mutation on the individuals whose fitness has just been measured. Discard the old population and iterate using the new population. One iteration of this loop is referred to as a GENERATION. There is no theoretical reason for this as an implementation model. Indeed, we do not see this punctuated behavior in populations in nature as a whole, but it is a convenient implementation model. The first generation (generation 0) of this process operates on a population of randomly generated individuals. From there on, the genetic operations, in concert with the fitness measure, operate to improve the population. PSEUDO CODE Algorithm GA is // start with an initial time t := 0; // initialize a usually random population of individuals initpopulation P (t); // evaluate fitness of all initial individuals of population evaluate P (t); // test for termination criterion (time, fitness, etc.) while not done do // increase the time counter t := t + 1; // select a sub-population for offspring production P' := selectparents P (t); // recombine the "genes" of selected parents recombine P' (t); // perturb the mated population stochastically mutate P' (t); // evaluate it's new fitness evaluate P' (t); // select the survivors from actual fitness P := survive P,P' (t); od end GA. ------------------------------ Subject: Q1.2: What's Evolutionary Programming (EP)? Introduction EVOLUTIONARY PROGRAMMING, originally conceived by Lawrence J. Fogel in 1960, is a stochastic OPTIMIZATION strategy similar to GENETIC ALGORITHMs, but instead places emphasis on the behavioral linkage between PARENTs and their OFFSPRING, rather than seeking to emulate specific GENETIC OPERATORS as observed in nature. Evolutionary programming is similar to EVOLUTION STRATEGIES, although the two approaches developed independently (see below). Like both ES and GAs, EP is a useful method of optimization when other techniques such as gradient descent or direct, analytical discovery are not possible. Combinatoric and real-valued FUNCTION OPTIMIZATION in which the optimization surface or FITNESS landscape is "rugged", possessing many locally optimal solutions, are well suited for evolutionary programming. History The 1966 book, "Artificial Intelligence Through Simulated Evolution" by Fogel, Owens and Walsh is the landmark publication for EP applications, although many other papers appear earlier in the literature. In the book, finite state automata were evolved to predict symbol strings generated from Markov processes and non- stationary time series. Such evolutionary prediction was motivated by a recognition that prediction is a keystone to intelligent behavior (defined in terms of adaptive behavior, in that the intelligent organism must anticipate events in order to adapt behavior in light of a goal). In 1992, the First Annual Conference on evolutionary programming was held in La Jolla, CA. Further conferences have been held annually (See Q12). The conferences attract a diverse group of academic, commercial and military researchers engaged in both developing the theory of the EP technique and in applying EP to a wide range of optimization problems, both in engineering and biology. Rather than list and analyze the sources in detail, several fundamental sources are listed below which should serve as good pointers to the entire body of work in the field. The Process For EP, like GAs, there is an underlying assumption that a fitness landscape can be characterized in terms of variables, and that there is an optimum solution (or multiple such optima) in terms of those variables. For example, if one were trying to find the shortest path in a Traveling Salesman Problem, each solution would be a path. The length of the path could be expressed as a number, which would serve as the solution's fitness. The fitness landscape for this problem could be characterized as a hypersurface proportional to the path lengths in a space of possible paths. The goal would be to find the globally shortest path in that space, or more practically, to find very short tours very quickly. The basic EP method involves 3 steps (Repeat until a threshold for iteration is exceeded or an adequate solution is obtained): (1) Choose an initial POPULATION of trial solutions at random. The number of solutions in a population is highly relevant to the speed of optimization, but no definite answers are available as to how many solutions are appropriate (other than >1) and how many solutions are just wasteful. (2) Each solution is replicated into a new population. Each of these offspring solutions are mutated according to a distribution of MUTATION types, ranging from minor to extreme with a continuum of mutation types between. The severity of MUTATION is judged on the basis of the functional change imposed on the parents. (3) Each offspring solution is assessed by computing it's fitness. Typically, a stochastic tournament is held to determine N solutions to be retained for the population of solutions, although this is occasionally performed deterministically. There is no requirement that the population size be held constant, however, nor that only a single offspring be generated from each parent. It should be pointed out that EP typically does not use any CROSSOVER as a GENETIC OPERATOR. EP and GAs There are two important ways in which EP differs from GAs. First, there is no constraint on the representation. The typical GA approach involves encoding the problem solutions as a string of representative tokens, the GENOME. In EP, the representation follows from the problem. A neural network can be represented in the same manner as it is implemented, for example, because the mutation operation does not demand a linear encoding. (In this case, for a fixed topology, real- valued weights could be coded directly as their real values and mutation operates by perturbing a weight vector with a zero mean multivariate Gaussian perturbation. For variable topologies, the architecture is also perturbed, often using Poisson distributed additions and deletions.) Second, the mutation operation simply changes aspects of the solution according to a statistical distribution which weights minor variations in the behavior of the offspring as highly probable and substantial variations as increasingly unlikely. Further, the severity of mutations is often reduced as the global optimum is approached. There is a certain tautology here: if the global optimum is not already known, how can the spread of the mutation operation be damped as the solutions approach it? Several techniques have been proposed and implemented which address this difficulty, the most widely studied being the "Meta-Evolutionary" technique in which the variance of the mutation distribution is subject to mutation by a fixed variance mutation operator and evolves along with the solution. EP and ES The first communication between the evolutionary programming and EVOLUTION STRATEGY groups occurred in early 1992, just prior to the first annual EP conference. Despite their independent development over 30 years, they share many similarities. When implemented to solve real-valued function optimization problems, both typically operate on the real values themselves (rather than any coding of the real values as is often done in GAs). Multivariate zero mean Gaussian mutations are applied to each parent in a population and a SELECTION mechanism is applied to determine which solutions to remove (i.e., "cull") from the population. The similarities extend to the use of self-adaptive methods for determining the appropriate mutations to use -- methods in which each parent carries not only a potential solution to the problem at hand, but also information on how it will distribute new trials (offspring). Most of the theoretical results on convergence (both asymptotic and velocity) developed for ES or EP also apply directly to the other. The main differences between ES and EP are: 1. Selection: EP typically uses stochastic selection via a tournament. Each trial solution in the population faces competition against a preselected number of opponents and receives a "win" if it is at least as good as its opponent in each encounter. Selection then eliminates those solutions with the least wins. In contrast, ES typically uses deterministic selection in which the worst solutions are purged from the population based directly on their function evaluation. 2. RECOMBINATION: EP is an abstraction of EVOLUTION at the level of reproductive populations (i.e., SPECIES) and thus no recombination mechanisms are typically used because recombination does not occur between species (by definition: see Mayr's biological species concept). In contrast, ES is an abstraction of evolution at the level of INDIVIDUAL behavior. When self-adaptive information is incorporated this is purely genetic information (as opposed to phenotypic) and thus some forms of recombination are reasonable and many forms of recombination have been implemented within ES. Again, the effectiveness of such operators depends on the problem at hand. References Some references which provide an excellent introduction (by no means extensive) to the field, include: Artificial Intelligence Through Simulated Evolution [Fogel66] (primary) Fogel DB (1995) "Evolutionary Computation: Toward a New Philosophy of Machine Intelligence," IEEE Press, Piscataway, NJ. Proceeding of the first [EP92], second [EP93] and third [EP94] Annual Conference on Evolutionary Programming (primary) (See Q12). PSEUDO CODE Algorithm EP is // start with an initial time t := 0; // initialize a usually random population of individuals initpopulation P (t); // evaluate fitness of all initial individuals of population evaluate P (t); // test for termination criterion (time, fitness, etc.) while not done do // perturb the whole population stochastically P'(t) := mutate P (t); // evaluate it's new fitness evaluate P' (t); // stochastically select the survivors from actual fitness P(t+1) := survive P(t),P'(t); // increase the time counter t := t + 1; od end EP. [Eds note: An extended version of this introduction is available from ENCORE (see Q15.3) in /FAQ/supplements/what-is-ep ] ------------------------------ Subject: Q1.3: What's an Evolution Strategy (ES)? In 1963 two students at the Technical University of Berlin (TUB) met and were soon to collaborate on experiments which used the wind tunnel of the Institute of Flow Engineering. During the search for the optimal shapes of bodies in a flow, which was then a matter of laborious intuitive experimentation, the idea was conceived of proceeding strategically. However, attempts with the coordinate and simple gradient strategies (cf Q5) were unsuccessful. Then one of the students, Ingo Rechenberg, now Professor of Bionics and Evolutionary Engineering, hit upon the idea of trying random changes in the parameters defining the shape, following the example of natural MUTATIONs. The EVOLUTION STRATEGY was born. A third student, Peter Bienert, joined them and started the construction of an automatic experimenter, which would work according to the simple rules of mutation and SELECTION. The second student, Hans-Paul Schwefel, set about testing the efficiency of the new methods with the help of a Zuse Z23 computer; for there were plenty of objections to these "random strategies." In spite of an occasional lack of financial support, the Evolutionary Engineering Group which had been formed held firmly together. Ingo Rechenberg received his doctorate in 1970 (Rechenberg 73). It contains the theory of the two membered EVOLUTION strategy and a first proposal for a multimembered strategy which in the nomenclature introduced here is of the (m+1) type. In the same year financial support from the Deutsche Forschungsgemeinschaft (DFG, Germany's National Science Foundation) enabled the work, that was concluded, at least temporarily, in 1974 with the thesis "Evolutionsstrategie und numerische Optimierung" (Schwefel 77). Thus, EVOLUTION STRATEGIEs were invented to solve technical OPTIMIZATION problems (TOPs) like e.g. constructing an optimal flashing nozzle, and until recently ES were only known to civil engineering folks, as an alternative to standard solutions. Usually no closed form analytical objective function is available for TOPs and hence, no applicable optimization method exists, but the engineer's intuition. The first attempts to imitate principles of organic evolution on a computer still resembled the iterative optimization methods known up to that time (cf Q5): In a two-membered or (1+1) ES, one PARENT generates one OFFSPRING per GENERATION by applying NORMALLY DISTRIBUTED mutations, i.e. smaller steps occur more likely than big ones, until a child performs better than its ancestor and takes its place. Because of this simple structure, theoretical results for STEPSIZE control and CONVERGENCE VELOCITY could be derived. The ratio between successful and all mutations should come to 1/5: the so- called 1/5 SUCCESS RULE was discovered. This first algorithm, using mutation only, has then been enhanced to a (m+1) strategy which incorporated RECOMBINATION due to several, i.e. m parents being available. The mutation scheme and the exogenous stepsize control were taken across unchanged from (1+1) ESs. Schwefel later generalized these strategies to the multimembered ES now denoted by (m+l) and (m,l) which imitates the following basic principles of organic evolution: a POPULATION, leading to the possibility of recombination with random mating, mutation and selection. These strategies are termed PLUS STRATEGY and COMMA STRATEGY, respectively: in the plus case, the parental generation is taken into account during selection, while in the comma case only the offspring undergoes selection, and the parents die off. m (usually a lowercase mu, denotes the population size, and l, usually a lowercase lambda denotes the number of offspring generated per generation). Or to put it in an utterly insignificant and hopelessly outdated language: (define (Evolution-strategy population) (if (terminate? population) population (evolution-strategy (select (cond (plus-strategy? (union (mutate (recombine population)) population)) (comma-strategy? (mutate (recombine population)))))))) However, dealing with ES is sometimes seen as "strong tobacco," for it takes a decent amount of probability theory and applied STATISTICS to understand the inner workings of an ES, while it navigates through the hyperspace of the usually n-dimensional problem space, by throwing hyperelipses into the deep... Luckily, this medium doesn't allow for much mathematical ballyhoo; the author therefore has to come up with a simple but brilliantly intriguing explanation to save the reader from falling asleep during the rest of this section, so here we go: Imagine a black box. A large black box. As large as, say for example, a Coca-Cola vending machine. Now, [..] (to be continued) A single INDIVIDUAL of the ES' population consists of the following GENOTYPE representing a point in the SEARCH SPACE: OBJECT VARIABLES Real-valued x_i have to be tuned by recombination and mutation such that an objective function reaches its global optimum. Referring to the metaphor mentioned previously, the x_i represent the regulators of the alien Coka-Cola vending machine. STRATEGY VARIABLEs Real-valued s_i (usually denoted by a lowercase sigma) or mean stepsizes determine the mutability of the x_i. They represent the STANDARD DEVIATION of a (0, s_i) GAUSSIAN DISTRIBUTION (GD) being added to each x_i as an undirected mutation. With an "expectancy value" of 0 the parents will produce offspring similar to themselves on average. In order to make a doubling and a halving of a stepsize equally probable, the s_i mutate log-normally, distributed, i.e. exp(GD), from generation to generation. These stepsizes hide the internal model the population has made of its ENVIRONMENT, i.e. a SELF-ADAPTATION of the stepsizes has replaced the exogenous control of the (1+1) ES. This concept works because selection sooner or later prefers those individuals having built a good model of the objective function, thus producing better offspring. Hence, learning takes place on two levels: (1) at the genotypic, i.e. the object and strategy variable level and (2) at the phenotypic level, i.e. the FITNESS level. Depending on an individual's x_i, the resulting objective function value f(x), where x denotes the vector of objective variables, serves as the PHENOTYPE (fitness) in the selection step. In a plus strategy, the m best of all (m+l) individuals survive to become the parents of the next generation. Using the comma variant, selection takes place only among the l offspring. The second scheme is more realistic and therefore more successful, because no individual may survive forever, which could at least theoretically occur using the plus variant. Untypical for conventional optimization algorithms and lavish at first sight, a comma strategy allowing intermediate deterioration performs better! Only by forgetting highly fit individuals can a permanent adaptation of the stepsizes take place and avoid long stagnation phases due to misadapted s_i's. This means that these individuals have built an internal model that is no longer appropriate for further progress, and thus should better be discarded. By choosing a certain ratio m/l, one can determine the convergence property of the evolution strategy: If one wants a fast, but local convergence, one should choose a small HARD SELECTION, ratio, e.g. (5,100), but looking for the global optimum, one should favour a softer selection (15,100). Self-adaptation within ESs depends on the following agents (Schwefel 87): Randomness: One cannot model mutation as a purely random process. This would mean that a child is completely independent of its parents. Population size: The population has to be sufficiently large. Not only the current best should be allowed to reproduce, but a set of good individuals. Biologists have coined the term "requisite variety" to mean the genetic variety necessary to prevent a SPECIES from becoming poorer and poorer genetically and eventually dying out. COOPERATION: In order to exploit the effects of a population (m > 1), the individuals should recombine their knowledge with that of others (cooperate) because one cannot expect the knowledge to accumulate in the best individual only. Deterioration: In order to allow better internal models (stepsizes) to provide better progress in the future, one should accept deterioration from one generation to the next. A limited life- span in nature is not a sign of failure, but an important means of preventing a species from freezing genetically. ESs prove to be successful when compared to other iterative methods on a large number of test problems (Schwefel 77). They are adaptable to nearly all sorts of problems in optimization, because they need very little information about the problem, especially no derivatives of the objective function. For a list of some 300 applications of EAs, see the SyS-2/92 report (cf Q14). ESs are capable of solving high dimensional, multimodal, nonlinear problems subject to linear and/or nonlinear constraints. The objective function can also, e.g. be the result of a SIMULATION, it does not have to be given in a closed form. This also holds for the constraints which may represent the outcome of, e.g. a finite elements method (FEM). ESs have been adapted to VECTOR OPTIMIZATION problems (Kursawe 92), and they can also serve as a heuristic for NP-complete combinatorial problems like the TRAVELLING SALESMAN PROBLEM or problems with a noisy or changing response surface. References Kursawe, F. (1992) " Evolution strategies for vector optimization", Taipei, National Chiao Tung University, 187-193. Kursawe, F. (1994) " Evolution strategies: Simple models of natural processes?", Revue Internationale de Systemique, France (to appear). Rechenberg, I. (1973) "Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution", Stuttgart: Fromman-Holzboog. Schwefel, H.-P. (1977) "Numerische Optimierung von Computermodellen mittels der Evolutionsstrategie", Basel: Birkhaeuser. Schwefel, H.-P. (1987) "Collective Phaenomena in Evolutionary Systems", 31st Annu. Meet. Inter'l Soc. for General System Research, Budapest, 1025-1033. ------------------------------ Subject: Q1.4: What's a Classifier System (CFS)? The name of the Game First, a word on naming conventions is due, for no other paradigm of EC has undergone more changes to it's name space than this one. Initially, Holland called his cognitive models "Classifier Systems" abbrv. with CS, and sometimes CFS, as can be found in [GOLD89]. Whence Riolo came into play in 1986 and Holland added a reinforcement component to the overall design of a CFS, that emphasized its ability to learn. So, the word "learning" was prepended to the name, to make: "Learning Classifier Systems" (abbrv. to LCS). On October 6-9, 1992 the "1st Inter'l Workshop on Learning Classifier Systems" took place at the NASA Johnson Space Center, Houston, TX. A summary of this "summit" of all leading researchers in LCS can be found on ENCORE (See Q15.3) in file CFS/papers/ Today, the story continues, LCSs are sometimes subsumed under a "new" machine learning paradigm called "Evolutionary Reinforcement Learning" or ERL for short, incorporating LCSs, "Q-Learning", devised by Watkins (1989), and a paradigm of the same name, devised by Ackley and Littman [ALIFEIII]. And then, this latter statement is really somewhat confusing, as Marco Dorigo has pointed out in a letter to editors of this guide, since Q-Learning has no evolutionary component. So please let the Senior Editor explain: When I wrote this part of the guide, I just had in mind that Q-Learning would make for a pretty good replacement of Holland's bucket-brigade algorithm, so I used this litte speculation to see what comes out of it; in early December 95, almost two years later, it has finally caught Marco's attention. But meanwhile, I have been proven right: Wilson has suggested to use Q- Learning in CLASSIFIER SYSTEM (Wilson 1994) and Dorigo & Bersini (1994) have shown that Q-Learning and the bucket-brigade are truly equivalent concepts. We would therefore be allowed to call a CFS that uses Q-Learning for rule discovery, rather than a bucket-brigade, a Q-CFS, Q-LCS, or Q- CS; in any case would the result be subsumed under the term ERL, even if Q-Learning itself is not an evolutionary algorithm! Interestingly, Wilson called his system ZCS (apparantly no "Q" inside), while Dorigo & Bersini called their system a D-Max-VSCS, or "discounted max very simple classifier system" (and if you know Q- Learning at least the D-Max part of the name will remind you of that algorithm). The latter paper can be found on Encore (see Q15.3) in file CFS/papers/ And by the way in [HOLLAND95] the term "classifier system" is not used anymore. You cannot find it in the index. It's a gone! Holland now stresses the adaptive component of his invention, and simply calls the resulting systems adaptive agents. These agents are then implemented within the framework of his recent invention called ECHO. (See for more.) On Schema Processors and ANIMATS So, to get back to the question above, "What are CFSs?", we first might answer, "Well, there are many interpretations of Holland's ideas...what do you like to know in particular?" And then we'd start with a recitation from [HOLLAND75], [HOLLAND92], and explain all the SCHEMA processors, the broadcast language, etc. But, we will take a more comprehensive, and intuitive way to understand what CLASSIFIER SYSTEMs are all about. The easiest road to explore the very nature of classifier systems, is to take the animat (ANIMAl + ROBOT = ANIMAT) "lane" devised by Booker (1982) and later studied extensively by Wilson (1985), who also coined the term for this approach. Work continues on animats but is often regarded as ARTIFICIAL LIFE rather than EVOLUTIONARY COMPUTATION. This thread of research has even its own conference series: "Simulation of Adaptive Behavior (SAB)" (cf Q12), including other notions from machine learning, connectionist learning, evolutionary robotics, etc. [NB: the latter is obvious, if an animat lives in a digital microcosm, it can be put into the real world by implantation into an autonomous robot vehicle, that has sensors/detectors (camera eyes, whiskers, etc.) and effectors (wheels, robot arms, etc.); so all that's needed is to use our algorithm as the "brain" of this vehicle, connecting the hardware parts with the software learning component. For a fascinating intro to the field see, e.g. Braitenberg (1984)] classifier systems, however, are yet another offspring of John Holland's aforementioned book, and can be seen as one of the early applications of GAs, for CFSs use this evolutionary algorithm to adapt their behavior toward a changing ENVIRONMENT, as is explained below in greater detail. Holland envisioned a cognitive system capable of classifying the goings on in its environment, and then reacting to these goings on appropriately. So what is needed to build such a system? Obviously, we need (1) an environment; (2) receptors that tell our system about the goings on; (3) effectors, that let our system manipulate its environment; and (4) the system itself, conveniently a "black box" in this first approach, that has (2) and (3) attached to it, and "lives" in (1). In the animat approach, (1) usually is an artificially created digital world, e.g. in Booker's Gofer system, a 2 dimensional grid that contains "food" and "poison". And the Gofer itself, that walks across this grid and tries (a) to learn to distinguish between these two items, and (b) survive well fed. Much the same for Wilson's animat, called "*". Yes, it's just an asterisk, and a "Kafka-esque naming policy" is one of the sign posts of the whole field; e.g. the first implementation by Holland and Reitmann 1978 was called CS-1, (cognitive system 1); Smith's Poker player LS-1 (1980) followed this "convention". Riolo's 1988 LCS implementations on top of his CFS-C library (cf Q20), were dubbed FSW-1 (Finite State World 1), and LETSEQ-1 (LETter SEQuence predictor 1). So from the latter paragraph we can conclude that environment can also mean something completely different (e.g. an infinite stream of letters, time serieses, etc.) than in the animat approach, but anyway; we'll stick to it, and go on. Imagine a very simple animat, e.g. a simplified model of a frog. Now, we know that frogs live in (a) Muppet Shows, or (b) little ponds; so we chose the latter as our demo environment (1); and the former for a non-Kafka-esque name of our model, so let's dub it "Kermit". Kermit has eyes, i.e. sensorial input detectors (2); hands and legs, i.e. environment-manipulating effectors (3); is a spicy-fly- detecting-and-eating device, i.e. a frog (4); so we got all the 4 pieces needed. Inside the Black Box The most primitive "black box" we can think of is a computer. It has inputs (2), and outputs (3), and a message passing system inbetween, that converts (i.e., computes), certain input messages into output messages, according to a set of rules, usually called the "program" of that computer. From the theory of computer science, we now borrow the simplest of all program structures, that is something called "production system" or PS for short. A PS has been shown to be computationally complete by Post (1943), that's why it is sometimes called a "Post System", and later by Minsky (1967). Although it merely consists of a set of if-then rules, it still resembles a full- fledged computer. We now term a single "if-then" rule a "classifier", and choose a representation that makes it easy to manipulate these, for example by encoding them into binary strings. We then term the set of classifiers, a "classifier population", and immediately know how to breed new rules for our system: just use a GA to generate new rules/classifiers from the current POPULATION! All that's left are the messages floating through the black box. They should also be simple strings of zeroes and ones, and are to be kept in a data structure, we call "the message list". With all this given, we can imagine the goings on inside the black box as follows: The input interface (2) generates messages, i.e., 0/1 strings, that are written on the message list. Then these messages are matched against the condition-part of all classifiers, to find out which actions are to be triggered. The message list is then emptied, and the encoded actions, themselves just messages, are posted to the message list. Then, the output interface (3) checks the message list for messages concerning the effectors. And the cycle restarts. Note, that it is possible in this set-up to have "internal messages", because the message list is not emptied after (3) has checked; thus, the input interface messages are added to the initially empty list. (cf Algorithm CFS, LCS below) The general idea of the CFS is to start from scratch, i.e., from tabula rasa (without any knowledge) using a randomly generated classifier population, and let the system learn its program by induction, (cf Holland et al. 1986), this reduces the input stream to recurrent input patterns, that must be repeated over and over again, to enable the animat to classify its current situation/context and react on the goings on appropriately. What does it need to be a frog? Let's take a look at the behavior emitted by Kermit. It lives in its digital microwilderness where it moves around randomly. [NB: This seemingly "random" behavior is not that random at all; for more on instinctive, i.e., innate behavior of non-artificial animals see, e.g. Tinbergen (1951)] Whenever a small flying object appears, that has no stripes, Kermit should eat it, because it's very likely a spicy fly, or other flying insect. If it has stripes, the insect is better left alone, because Kermit had better not bother with wasps, hornets, or bees. If Kermit encounters a large, looming object, it immediately uses its effectors to jump away, as far as possible. So, part of these behavior patterns within the "pond world", in AI sometimes called a "frame," from traditional knowledge representation techniques, Rich (1983), can be expressed in a set of "if then " rules, as follows: IF small, flying object to the left THEN send @ IF small, flying object to the right THEN send % IF small, flying object centered THEN send $ IF large, looming object THEN send ! IF no large, looming object THEN send * IF * and @ THEN move head 15 degrees left IF * and % THEN move head 15 degrees right IF * and $ THEN move in direction head pointing IF ! THEN move rapidly away from direction head pointing Now, this set of rules has to be encoded for use within a CLASSIFIER SYSTEM. A possible encoding of the above rule set in CFS-C (Riolo) classifier terminology. The condition part consists of two conditions, that are combined with a logical AND, thus must be met both to trigger the associated action. This structure needs a NOT operation, (so we get NAND, and know from hardware design, that we can build any computer solely with NANDs), in CFS-C this is denoted by the `~' prefix character (rule #5). IF THEN 0000, 00 00 00 00 0000, 00 01 00 01 0000, 00 10 00 10 1111, 01 ## 11 11 ~1111, 01 ## 10 00 1000, 00 00 01 00 1000, 00 01 01 01 1000, 00 10 01 10 1111, ## ## 01 11 Obviously, string `0000' denotes small, and `00' in the fist part of the second column, denotes flying. The last two bits of column #2 encode the direction of the object approaching, where `00' means left, `01' means right, etc. In rule #4 a the "don't care symbol" `#' is used, that matches `1' and `0', i.e., the position of the large, looming object, is completely arbitrary. A simple fact, that can save Kermit's (artificial) life. PSEUDO CODE (Non-Learning CFS) Algorithm CFS is // start with an initial time t := 0; // an initially empty message list initMessageList ML (t); // and a randomly generated population of classifiers initClassifierPopulation P (t); // test for cycle termination criterion (time, fitness, etc.) while not done do // increase the time counter t := t + 1; // 1. detectors check whether input messages are present ML := readDetectors (t); // 2. compare ML to the classifiers and save matches ML' := matchClassifiers ML,P (t); // 3. process new messages through output interface ML := sendEffectors ML' (t); od end CFS. To convert the previous, non-learning CFS into a learning CLASSIFIER SYSTEM, LCS, as has been proposed in Holland (1986), it takes two steps: (1) the major cycle has to be changed such that the activation of each classifier depends on some additional parameter, that can be modified as a result of experience, i.e. reinforcement from the ENVIRONMENT; (2) and/or change the contents of the classifier list, i.e., generate new classifiers/rules, by removing, adding, or combining condition/action-parts of existing classifiers. The algorithm thus changes accordingly: PSEUDO CODE (Learning CFS) Algorithm LCS is // start with an initial time t := 0; // an initially empty message list initMessageList ML (t); // and a randomly generated population of classifiers initClassifierPopulation P (t); // test for cycle termination criterion (time, fitness, etc.) while not done do // increase the time counter t := t + 1; // 1. detectors check whether input messages are present ML := readDetectors (t); // 2. compare ML to the classifiers and save matches ML' := matchClassifiers ML,P (t); // 3. highest bidding classifier(s) collected in ML' wins the // "race" and post the(ir) message(s) ML' := selectMatchingClassifiers ML',P (t); // 4. tax bidding classifiers, reduce their strength ML' := taxPostingClassifiers ML',P (t); // 5. effectors check new message list for output msgs ML := sendEffectors ML' (t); // 6. receive payoff from environment (REINFORCEMENT) C := receivePayoff (t); // 7. distribute payoff/credit to classifiers (e.g. BBA) P' := distributeCredit C,P (t); // 8. Eventually (depending on t), an EA (usually a GA) is // applied to the classifier population if criterion then P := generateNewRules P' (t); else P := P' od end LCS. What's the problem with CFSs? Just to list the currently known problems that come with CFSs, would take some additional pages; therefore only some interesting papers dealing with unresolved riddles are listed; probably the best paper containing most of these is the aforementioned summary of the LCS Workshop: Smith, R.E. (1992) "A report on the first Inter'l Workshop on LCSs" avail. from ENCORE (See Q15.3) in file CFS/papers/ Other noteworthy critiques on LCSs include: Wilson, S.W. (1987) "Classifier Systems and the Animat Problem" Machine Learning, 2. Wilson, S.W. (1988) "Bid Competition and Specificity Reconsidered" Complex Systems, 2(5):705-723. Wilson, S.W. & Goldberg, D.E. (1989) "A critical review of classifier systems" [ICGA89], 244-255. Goldberg, D.E., Horn, J. & Deb, K. (1992) "What makes a problem hard for a classifier system?" (containing the Goldberg citation below) is also available from Encore (See Q15.3) in file CFS/papers/ Dorigo, M. (1993) "Genetic and Non-genetic Operators in ALECSYS" Evolutionary Computation, 1(2):151-164. The technical report, the journal article is based on is avail. from Encore (See Q15.3) in file CFS/papers/ Smith, R.E. Forrest, S. & Perelson, A.S. (1993) "Searching for Diverse, Cooperative POPULATIONs with Genetic Algorithms" Evolutionary Computation, 1(2):127-149. Conclusions? Generally speaking: "There's much to do in CFS research!" No other notion of EC provides more space to explore and if you are interested in a PhD in the field, you might want to take a closer look at CFS. However, be warned!, to quote Goldberg: "classifier systems are a quagmire---a glorious, wondrous, and inventing quagmire, but a quagmire nonetheless." References Booker, L.B. (1982) "Intelligent behavior as an adaption to the task environment" PhD Dissertation, Univ. of Michigan, Logic of Computers Group, Ann Arbor, MI. Braitenberg, V. (1984) "Vehicles: Experiments in Synthetic Psychology" Boston, MA: MIT Press. Dorigo M. & H. Bersini (1994). "A Comparison of Q-Learning and Classifier Systems." Proceedings of From Animals to Animats, Third International Conference on SIMULATION of Adaptive Behavior (SAB94), Brighton, UK, D.Cliff, P.Husbands, J.-A.Meyer and S.W.Wilson (Eds.), MIT Press, 248-255. Holland, J.H. (1986) "Escaping Brittleness: The possibilities of general-purpose learning algorithms applied to parallel rule-based systems". In: R.S. Michalski, J.G. Carbonell & T.M. Mitchell (eds), Machine Learning: An Artificial Intelligence approach, Vol II, 593-623, Los Altos, CA: Morgan Kaufman. Holland, J.H., et al. (1986) "Induction: Processes of Inference, Learning, and Discovery", Cambridge, MA: MIT Press. Holland, J.H. (1992) "Adaptation in natural and artificial systems" Boston, MA: MIT Press. Holland, J.H. (1995) "Hidden Order: How adaptation builds complexity" Reading, MA: Addison-Wesley. [HOLLAND95]: Holland, J.H. & Reitman, J.S. (1978) "Cognitive Systems based on Adaptive Algorithms" In D.A. Waterman & F.Hayes-Roth, (eds) Pattern- directed inference systems. NY: Academic Press. Minsky, M.L. (1961) "Steps toward Artificial Intelligence" Proceedings IRE, 49, 8-30. Reprinted in E.A. Feigenbaum & J. Feldman (eds) Computers and Thought, 406-450, NY: McGraw-Hill, 1963. Minsky, M.L. (1967) "Computation: Finite and Infinite Machines" Englewood Cliffs, NJ: Prentice-Hall. Post, Emil L. (1943) "Formal reductions of the general combinatorial decision problem" American Journal of Mathematics, 65, 197-215. Rich, E. (1983) "Artificial Intelligence" NY: McGraw-Hill. Tinbergen, N. (1951) "The Study of Instinct" NY: Oxford Univ. Press. Watkins, C. (1989) "Learning from Delayed Rewards" PhD Dissertation, Department of Psychology, Cambridge Univ., UK. Wilson, S.W. (1985) "Knowledge growth in an artificial animal" in [ICGA85], 16-23. Wilson, S.W. (1994) "ZCS: a zeroth level classifier system" in EC 2(1), 1-18. ------------------------------ Subject: Q1.5: What's Genetic Programming (GP)? GENETIC PROGRAMMING is the extension of the genetic model of learning into the space of programs. That is, the objects that constitute the POPULATION are not fixed-length character strings that encode possible solutions to the problem at hand, they are programs that, when executed, "are" the candidate solutions to the problem. These programs are expressed in genetic programming as parse trees, rather than as lines of code. Thus, for example, the simple program "a + b * c" would be represented as: + / \ a * / \ b c or, to be precise, as suitable data structures linked together to achieve this effect. Because this is a very simple thing to do in the programming language Lisp, many GPers tend to use Lisp. However, this is simply an implementation detail. There are straightforward methods to implement GP using a non-Lisp programming environment. The programs in the population are composed of elements from the FUNCTION SET and the TERMINAL SET, which are typically fixed sets of symbols selected to be appropriate to the solution of problems in the domain of interest. In GP the CROSSOVER operation is implemented by taking randomly selected subtrees in the INDIVIDUALs (selected according to FITNESS) and exchanging them. It should be pointed out that GP usually does not use any MUTATION as a GENETIC OPERATOR. More information is available in the GP mailing list FAQ. (See Q15.2) and from ------------------------------ Copyright (c) 1993-1996 by J. Heitkoetter and D. Beasley, all rights reserved. This FAQ may be posted to any USENET newsgroup, on-line service, or BBS as long as it is posted in its entirety and includes this copyright statement. This FAQ may not be distributed for financial gain. This FAQ may not be included in commercial collections or compilations without express permission from the author. End of ai-faq/genetic/part2 ***************************