Chapter 3: Evolutionary Computation Concepts and Paradigms

Chapter 3: Evolutionary Computation Concepts and Paradigms

Chapter 3: Evolutionary Computation Concepts and Paradigms Chapter 3: Outline Review of the EC field Provide info to use EC tools to solve practical problems Terminology and key concepts Paradigms Review history Discuss five main areas of EC

Evolutionary Computation History We will examine five areas of evolutionary computation: Genetic algorithms Evolutionary programming Evolution strategies Genetic programming Particle swarm optimization Most work has been done in genetic algorithms (GAs), so this history (arbitrarily) concentrates

there. Focus is on people: * A broad sample, not exhaustive * In chronological order for each field Genetic Algorithms A. S. Fraser Worked in 1950s in Australia Biologist using computers to simulate natural genetic system Worked on epistasis (suppression of the effect of a gene) Used a 15-bit string as representation; 5 bits per parameter

Selected parents according to fitness function values [-1, Didnt consider applications to artificial systems John Holland Has had more influence on GA field than any other person Has been at the Univ. of Michigan since the early 1960s Received first Ph.D. in Computer Science in the U. S. (under Arthur Burks) Holland created the genetic algorithm field Published and taught in the field of adaptive systems in 1960s Was a pioneer in the use of a population of individuals to do search Derived schema theorem which shows that more fit schema are

more likely to reproduce Used reproduction, crossover, and mutation as early as 1960s GA was originally called a genetic plan J. D. Bagley Student of John Holland First used term genetic algorithm in his 1967 dissertation Used Gas in game playing (Hexapawn) 1975 Holland published Adaptation in Natural and Artificial

Systems (2nd Edition in 1992) DeJongs dissertation Five test functions for GAs Metrics for convergence and ongoing performance Measured effects of varying population size, crossover probability, mutation rate, and generation gap. (DeJong went to U. Pittsburgh, then to Naval Research Lab.) John Grefenstette Student of DeJong at Pittsburgh Went to Vanderbilt where one of his students was Dave

Schaffer, who developed the first multiobjective GA Developed GENESIS, the back-propagation of GAs Instrumental in founding (and editing proceedings of) ICGA Now at NRL Dave Goldberg Student of John Holland Dissertation on gas pipeline project Published landmark book, Genetic Algorithms in Search, Optimization, and Machine Learning (1989) Now at Univ. of Illinois Urbana-Champaign

Lawrence (Dave) Davis Self-taught in GAs Used GAs for 2-D bin packing in chip layout at TI Edited Handbook of Genetic Algorithms Tutorial Case studies Software diskette: OOGA (Lisp) and Genesis Evolutionary Programming Larry J. Fogel Developed evolutionary programming (EP) in 1960s EP utilizes survival of fittest (or survival of more skillful), but

only operation on structures is mutation Interests in finite state machines and machine intelligence EP abstracts evolution as a top-down process of adaptive behavior, rather than a bottom-up process of adaptive genetics 1960s book was controversial EP suffered from lack of funding due to numerics versus symbolics controversy David Fogel and others now carrying on the work Evolution Strategies I. Rechenberg Developed Evolutionstrategie (evolution strategies) during

1960s in Germany ES uses recombination rather than crossover, and Gaussianbased mutation is done Worked on engineering optimization of airfoil designs Published book in 1973 that is foundation of ES. Student was H.-P. Schwefel, who developed computer simulations Student of Schwefel is Thomas Baeck, now a leader in the field Genetic Programming R. M. Friedberg Early (1950s) work related to genetic programming

Used one program to write and optimize another fixedlength program Each program was 64 instructions, each instruction 14 bits Results were disappointing: Program was judged to have failed if not successfully terminated in 64 instructions Population of only one Inappropriate fitness functions John Koza Student of John Holland Developed genetic programming (GP) in late 1980s

GP evolves computer programs Population of programs Tree structures Uses LISP symbolic expressions Published books and video tapes Particle Swarm Optimization A Social Psychology Paradigm Tour Latans dynamic social impact theory Axelrods culture model Kennedys adaptive culture model

Latans Dynamic Social Impact Theory Behaviors of individuals can be explained in terms of the selforganizing properties of their social systems Clusters of individuals develop similar beliefs Subpopulations diverge from one another (polarization) Dynamic Social Impact Theory Characteristics Consolidation: Opinion diversity is reduced as individuals are exposed to majority arguments

Clustering: Individuals become more like their neighbors in social space Correlation: Attitudes that were originally independent tend to become associated Continuing diversity: Clustering prevents minority views form complete consolidation Dynamic Social Impact Theory: Summary Individuals influence one another, and in doing so become

more similar Patterns of belief held by individuals tend to correlate within regions of a population This model is consistent with findings in the fields of social psychology, sociology, economics, and anthropology Axelrods Culture Model Populations of individuals are pictured as strings of symbols, or features Probability of interaction between two individuals is a function of their similarity

Individuals become more similar as a result of interactions The observed dynamic is polarization, homogeneous subpopulations that differ from one another Kennedys Adaptive Culture Model No effect of similarity on probability of interaction The effect of similarity is negative in that it is dissimilarity that creates boundaries between cultural regions Interaction occurs if fitnesses are different

Culture and Cognition Summary Individuals searching for solutions learn from the experiences of others (individuals learn from their neighbors) An observer of the population perceives phenomena of which the individuals are the parts (individuals that interact frequently become similar) Culture affects the performance of individuals that comprise it (individuals gain benefit by imitating their neighbors)

So, what about intelligence? Social behavior increases the ability of an individual to adapt There is a relationship between adaptability and intelligence Intelligence arises from interactions among individuals Toward unification 1994 IEEE World Congress on Computational Intelligence (WCCI) First ICEC chaired by Zbigniew Michalewicz 1998, 2002 and 2006 IEEE WCCIs Workers in different groups now working together

Evolutionary Computation Cover evolutionary computation theory and paradigms Emphasize use of EC to solve practical problems Compare with other techniques - see how EC fits in with other approache Definition: Evolutionary Computation Evolutionary computation consists of machine learning optimization and classification paradigms

that are roughly based on evolution mechanisms such as biological genetics and natural selection. The EC field comprises four main areas: genetic algorithms, evolutionary programming, evolution strategies and genetic programming. EC Paradigms are Unique EC paradigms differ from traditional search and optimization paradigms in that EC paradigms: 1) Use a population of points in their search, 2) Use direct fitness information, instead of function derivatives

or other related knowledge, and , 3) Use probabilistic rather than deterministic transition rules. Population Each member is a point in the hyperspace problem domain, and thus is a potential solution A new population is generated each epoch Population typically remains the same size Operators such as crossover and mutation significantly enhance

parallel search capabilities Fitness Information Auxiliary information such as derivatives used to minimize sum-squared error in neural nets is not used The fitness value optimized is directly proportional to the function value being optimized If fitness is proportional to profit, for example, then the fitness rises as the profit rises Probabilistic Transition Rules

Search is not random Search is directed toward regions that are likely to have higher fitness values Different EC paradigms make different uses of stochasticity Encoding of Parameters Often encoded as binary strings Any finite alphabet can be used Typically, population member string is of fixed length Generic EC Procedure

1. Initialize the population 2. Calculate the fitness for each individual 3. Reproduce selected individuals to form new population 4. Perform evolutionary operations (mutation, etc.) 5. Go to step 2 until some condition is met Generic EC Procedure, Contd. Initialization often done by randomizing initial population (can use promising values sometimes) Fitness value is proportional to value of function being optimized (often scaled 01) Selection for reproduction based on fitness values (some

paradigms such as PSO retain all population members) Applying EC Tools Optimization and classification Mostly optimization non-differentiable many local optima may not know optimum system may be dynamic, changing with time, or even chaotic Optimization versus meliorization (perhaps try other approaches first) Note: EC is usually the second-best way to solve a problem!

EC Tools are Robust Can be used to solve many problems, and many kinds of problems, with minimal adjustments Are fast and easy to implement Genetic Algorithms: Outline Overview Terminology GA example problem Review of GA operations Schemata and the schema theorem

Genetic Algorithms Reflect in a primitive way some of the natural processes of evolution GAs perform highly efficient search of the problem hyperspace GAs work with a population of individuals (chromosomes) Number of elements in each individual equals the number of parameters in the optimization problem If w is the number of parameters, and we have b bits per parameter, then the search space is 2wb The variables being optimized comprise the phenotype space.

The binary strings upon which the operators work comprise the genotype space. General Procedure for Implementing a GA 1. Initialize the population (usually randomized binary strings) 2. Calculate the fitness for each individual in the population 3. Reproduce selected individuals to form new population 4. Perform crossover and mutation (or other operations) 5. Loop to step 2 until some condition is met Implementing a Simple GA

Involves: Fitness evaluation Copying strings Exchanging portions of strings Flipping bits in strings GA Example Problem x 256

Optimize value of f ( x ) sin over 0 x 255 where x is restricted to integers. The maximum value of 1 occurs at x = 128. Note: Function space is identical to fitness GA Example Problem

One variable Use binary alphabet Therefore, represent each individual as 8-bit binary string Set number of population members to (artificially low) 8 Randomize population Calculate fitness for each member of (initialized) population GA Example Problem GA Example Problem Reproduction Reproduction is used to form new population of n individuals Select members of current population

Use stochastic process based on fitnesses First, compute normalized fitness value for each individual by dividing individual fitness by sum of all fitnesses (fi / 5.083 in the example case) Generate a random number between 0 and 1 n times to form new population (sort of like spinning roulette wheel) Roulette Wheel Selection Population after Reproduction The eight random numbers generated, presented in random order, are .293, .971, .160, .469, .664, .568, .371,

and .109. This results in initial population member numbers 3, 8, 2, 5, 6, 5, 3, and 1 being chosen to make up the population after reproduction, as shown below. GA Example Problem: Crossover Crossover: the exchange of portions of two individuals (the parents) Probability of crossover usually ~ .65 - .80, .75 used here Randomly pair off population, determine whether or not crossover will occur (in example, 3 of 4 crossover) Randomly select two crossover points for pairs undergoing crossover

Exchange portions of strings between 1st and 2nd crossover points, treating individuals as ring structures Crossover: Before and After GA Example Problem: Mutation Mutation is final operation in Gas Consists of flipping bits with probability of flip ~ .001 - .01 Example problem, assume no bit flips (64 bits total in population) Now have first generation, with total fitness of 6.313, and two individuals each with fitness > .99

Now ready for more generations until stopping condition met: * Number of generations * Population member(s) with > specified fitness * No change in max fitness in n generations Summary of GA Process 1. 2. 3. 4. 5.

6. 7. 8. 9. 10. Select the initial population (usually randomly). Select percent probability of crossover (often .6-.8) and of mutation (often about .001). Calculate the fitness value for each population member. Normalize fitness values and use to determine probabilities for reproduction.

Reproduce new generation with the same number of members, using probabilities from 3. Pair off strings to cross over randomly. Select crossing sites (often 2) randomly for each pair. Mutate on a bit-by-bit basis. If more generations, go to step 2. If completed, stop and output results. Review of GA Operations Representation of variables Population size Population initialization

Fitness calculation Reproduction Crossover Inversion Mutation Selecting number of generations Representation of Variables Consider example problem, where 127 is 01111111 and 128 is 10000000 The smallest fitness change requires change in every bit Gray coding has representation such that adjacent values

vary by a single bit Some software converts dynamic range and resolution into appropriate bit strings Different alphabets possible Gray Coding To get Gray code from binary code, the leftmost bit is the same, then Gi = XOR(Bi, Bi-1) for i>=2, where Gi is the ith Gray code bit, Bi is ith binary bit. Dynamic Range and Resolution

Some software allows you to set the dynamic range and resolution for each variable. Example: Dynamic range is 2.5 to 4.5, and you want a resolution of 3 decimal places (1 in 1000), you need 11 bits. Problems can occur: If we have a dynamic range of 5 and a resolution of 3 decimal places, then we need 13 bits, the same as for a dynamic range of 8. We can now get numbers from crossover and mutation out of range, and repairs or penalties must be implemented. Population Size

Start with moderate sized population 50-500 is often a good starting place for a GA Population size tend to increase linearly with individual string length (not exponentially) Population Initialization Usually selected stochastically Sometimes seeded with a few promising individuals Do not skew population significantly Fitness Calculation Most GAs scale fitness values

Example problem illustrates two common problems: 1. Bunching of fitness values near top of scale, thereby lowering fitness differentials 2. Fitness values often are nearly constant Fitness Calculation, Contd. Solve bunching problem by: *Equally spacing fitness values (dont use 0, and keep track of f(x) for evaluation purposes) *Use scaling window over last w (~5-10) generations that keeps track of minimum fitness value

Solve flat fitness problem (in problems like example problem) by n defining a new f(x): f ( x )new f ( x )old Accomplish minimization with a GA by setting fitness equal to fmax f(x), where fmax is set by scaling Reproduction Reproduction often done by normalizing fitnesses and generating n random numbers between 0 and 1 Bakers method: Use roulette wheel with n pointers spaced 1/n apart; use normalized fitness; spin wheel once. Tournament selection - Select two individuals at random; the individual with the higher fitness is selected

for the next population Generation gap approach: Replace x percent that have worst fitness values (x is defined as the generation gap) Elitist strategy ensures that individual with highest fitness is copied into next generation (most GAs use this) Bakers Method: Two Examples Crossover Two-point crossover, with p(c) of 60-80% is common Often start with relatively high crossover rate, and reduce it during the run

The most basic crossover is one-point: 10110|010 > 10110100 01001|100 > 01001010 Uniform crossover (Syswerda) * Randomly choose 2 parents & stochastically decide whether to do crossover * At each bit position, exchange bits between the 2 strings with p(c) <= 50% [constant for run]

Matrix Crossover Example of matrix crossover after Bezdek (1994) Working with a population of c by n matrices, where c = no. of classes and n is number of patterns Each matrix represents classes assigned to all patterns (each column represents a pattern; only one 1 is allowed in each column) This case is analogous to working with strings that have an alphabet of c characters Example of Crossover Using

Matrix Function c=3 n=7 Inversion Single parent produces single child Not used much today...it destroys schemata (defined later) Example: 10|01101|0 -> 10101100 Mutation

Stochastically flipping bits often with p(m) ~ .001 If real-valued parameters used, mutation can assign any value in parameters allowed range Probability of mutation usually held constant or increased during run Can increase mutation rate when fitness variablility drops below some threshold Selecting the Number of Generations Often a trial and error process Optimum value (if known) a function of the problem

Multiple runs often done; overall best solution selected Schemata and the Schema Theorem Schemata are similarity templates for strings (features of strings) Schema: a subset of strings with identical values at specified string locations A dont care or wildcard symbol such as * or # is used in schema locations where the value doesnt matter The schema 1**0 has 4 matching strings: 1000, 1010, 1100, 1110

Schemata, contd. For a string of length l and alphabet of a characters, the total possible number of schemata is (a+1) l For a population of n individuals, there are between 2l and n2l unique schemata Schemata in highly fit individuals are more likely to be reproduced in the new population If 2l then all populatoin members are identical If n2l thenno two schemata are the same Population diversity is proportional to the number

of schemata Schemata, contd. Crossover and mutation are needed to guide search into new regions Effect of crossover dependent on defining length of schemata In case of 1****0 and **10**, the first is more likely to be disrupted by crossover Mutation is as likely to disrupt one as the other Short, highly fit, schemata will appear in increasing numbers in

successive generations The Schema Theorem The Schema Theorem predicts the number of times a specific schema will appear in the next generation of a GA, given the average fitness of individuals containing the schema, the average fitness of the population, and other parameters. Fundamental Theorem of Genetic Algorithms: Hollands Schema Theorem

Schema Theorem Consequences A genetic algorithm works with all schemata simultaneously, an attribute named intrinsic parallelism by Holland. The most rapidly increasing representation in any population will be of highly fit, short schemata, which will experience growth approaching exponential. The Schema Theorem does not indicate how well a GA will solve a particular problem.

Final Comment on GAs [T]here is something profoundly moving about linking a genetic algorithm to a difficult problem and returning later to find that the algorithm has evolved a solution that is better than the one a human found. With genetic algorithms we are not optimizing; we are creating conditions in which optimization occurs, as it may have occurred in the natural world. One feels a kind of resonance at such times that is uncommon and profound. Dave Davis (1991) (This could apply to other EC paradigms as well.)

Evolutionary Programming Similar to genetic algorithms (GAs) Uses population of solutions Evolves a solution using survival of the fittest Uses mutation (slightly differently than GAs) Different from GAs Concentrates on top-down processes of adaptive behavior Simulates evolution (phenotypic behavior) rather than genetics (genotypic behavior) Does not use crossover Works in phenotype space of observable behaviors

Generates the same number of children as parents Evolutionary Programming Procedure 1. 2. 3. 4. 5. 6. 7.

Initialize the population Expose the population to the environment Calculate the fitness for each member Randomly mutate each parent population member Evaluate parents and children Select members of new population Go to step 2 until some condition is met Population Initialization Component variables are usually real-valued Dynamic range constraints usually exist and are observed Random initial values within dynamic ranges are used

Population is often a few dozen to a few hundred Example Application Finite State Machine Evolution Illustrates use of EP for prediction Analyze a symbol sequence and predict next symbol - or otherwise optimize a fitness function Definition of Finite State Machine (Fogel): A transducer that can be stimulated by a finite alphabet of input symbols, can respond in a finite alphabet of output symbols, and possesses some finite number of internal states Can have different input and output symbol alphabets

Initial state must be specified; and specification table must be provided Three-state Finite State Machine Finite State Machine Evolution Finite state machines (FSMs) are similar to Turing machines, which can solve all mathematical problems of a defined general class EP uses only mutation; for FSMs, 5 main types of mutation exist: * Change initial state

* Delete a state * Add a state * Change a state transition * Change an output signal Note: first two mutations require that >1 state exist Finite State Machine Evolution Each parent produces one child; population temporarily doubles Typically keep best 1/2 of doubled population for new population Problems can exist with respect to state transition tables: * States can be added that are not utilized * Deleted states may make state transitions impossible

(can be fatal error) Finite State Machine Evolution Example of fixed length structure with maximum of 4 states, 2 input symbols, and 3 output symbols: Can use 9 bits per state; 36 bits total per population member bit 1: activity of state (enabled or not) bits 2 and 3: output symbol for input of 0 bits 4 & 5: next state for input of 0 bits 6 & 7: output symbol for input of 1 bits 8 & 9: next state for input of 1

(Note: must deal with non-existent output symbols and non-existent states.) Finite State Machine Evolution Lengths of individuals can either be variable (Fogel) or fixed One possible mutation method (given 5 possible kinds of mutation): 1. Generate random number between 0 and 1 2. If it is between 0 and .2, do first kind of mutation, if between .8 and 1.0, do fifth, etc.

3. Mutation is done across possible values with flat probability 4. Reassign infeasible state transitions with flat probabilities 5. Evaluate fitnesses Keep best 50% of population Number of mutations per individual, and probability of each of the five types of mutation can be varied...even evolved! Axelrods Iterated Prisoners Dilemma Payoff Function

Player 1 C D Player C 3|3 0|5 2

D 5|0 1|1 (Format is Player2| Player1) A 7-State Finite State Machine Function Optimization Mutation is done with Gaussian random function

Example: optimize F(x,y) = x2 + y2 , i.e., find the minimum at (0,0) Procedure: * Set population size (=50 for this problem) * Initialize values of variables over [-5,5] * Calculate fitness values (1/Euclidean_dist_fm_origin) * Mutate each parent to produce one child EP Mutation Process Function Optimization Mutation * Mutate by adding a Gaussian random variable with zero mean and variance equal to parents error to each

parent vector component * Next generation can be best 1/2, or can be selected probabilistically in tournament competitions with other individuals Tournament Selection for EP Example: Perform 10 competitions for each individual using error scores: 1. Randomly select opponent 2. Probability of scoring a point = error of opponent divided by sum of individual and opponent errors 3. Repeat 10 times

Each individual gets a total score; highest 50 percent survive to next generation Selection is thus a probabilistic function of fitness Evolution Strategies Evolution strategies field is based on the evolution of evolution (evolution optimizing itself) Evolution strategies utilize mutation and recombination (similar to crossover) Phenotypic behavior stressed, similar to EP Recombination uses real values for variables, rather than the binary coding of GAs

Strategy Parameters Individuals include strategy parameters that are variances and covariances that are evolved Each parameter can have a variance associated with it With n variables, there can be up to n variances Usually, only one is implemented Usually use standard deviation to set the mutation step size There are up to n(n-1)/2 covariances for each individual; covariances are usually not implemented

*Note: Both strategy parameters and individual variables are evolved Evolution Window Evolution window concept: Mutation improves fitness only if it occurs within a defined window If evolution operators stay within the window (std. dev. is optimal), probability of successful mutation is approximately 0.2 Evolving the window itself can result in metaevolution ES Mutation

Variables: Gaussian noise functions with zero mean determine mutation magnitudes for variables. Strategic parameters (variances and covariances): use log-normal distributions for standard deviations of strategic parameters Mutation rate is inversely proportional to number of variables in individual Mutation rate sometimes made proportional to error (distance from optimum; if opt. not known, even limited knowledge helps guide search) ES Recombination (Crossover)

* Recombination (crossover) manipulates entire variable values using one of two methods: * Local method - Form 1 new individual using two randomlyselected parents (usual method used) * Global method - Uses entire population as potential sources for each new individual * Global and local methods can each be implemented in one of two ways (often use local method): * Discrete recombination - Select value from one parent or the other (usually used for parameter values) * Intermediate recombination - Each parameter value for child is a point between parents values (used for strategy parameters)

new i x x old A ,i

C x old B ,i x old A ,i

(Often, C=0.5) Selection Methods Ranking Only the best are chosen Roulette Wheel Probability of selection is proportional to fitness Tournament Selection Local competitions determine survivors Best of random pair Best of random subgroup Winner of defined number of competitions (Note: Having n-1 tournaments is the same as ranking.)

ES Selection * ES usually operates with a surplus of descendents * Ratio of number of children to number of parents is often about 7 * Two main types of selection: , : : individuals with highest fitness out of children selected (parents not eligible) most fit out of and individuals selected

, version generally reported to give better performance (it is not elitist!) ES Procedure Summary 1.Initialize population 2.Perform recombination using the parents to form childre n 3. Mutate all children 4.Evaluate or population members 5. Select fittest for new population 6. If termination criteria not met, go to step 2

Genetic Programming Genetic programming (GP) evolves hierarchical computer programs Programs are represented as tree structure populations (most work originally done in Lisp) Structures may vary in size and complexity The goal is to obtain the desired output for a given set of inputs, within the search domain Differences between GPs and Generic GAs

Population members of GPs are executable structures (generally, computer programs) rather than strings of bits and/or variables The fitness of an individual population member in a GP is measured by executing it. (Generic GAs measure of fitness depends on the problem being solved.)

Hierarchical structure of GPs Representation and Preparation Each program is represented as a parse tree Functions are at internal tree points Variables and constants are at external (leaf) points Preliminary steps are to specify: Terminal set (variables and constants) Function set

Fitness measure System control parameters Termination conditions Terminal Set & Function Set Consists of system state variables and constants relevant to problem Example: cart centering problem uses position x, velocity v, and a constant such as 1 Functions selected are limited only by programming language implementation Math functions (sin, exp, ...)

Arithmetic functions Conditional operators (if...then, etc.) Boolean operators (AND, NOT, ...) Select a minimal yet sufficient set Arity of each must be honored Closure and Sufficiency * Closure - Each function must successfully operate on all functions and terminals (any value of any data type) * Closure requirement gives rise to protected

functions: functions redefined so as to return acceptable values (ex: divide by 0) Closure and Sufficiency Sufficiency - Set of functions and set of terminals must be sufficient to allow a solution to be evolved * Some knowledge of problem required * Some problems are easier than others - Boolean easy (AND, OR, NOT)

- Control system equations may be difficult, since having more than minimally sufficient function set usually degrades performance significantly (but sometimes improves it!) * Koza generally limits the number of terminals Fitness Measures and Control Parameters Variety of possible fitness measures

* Inversely proportional to error * Game score Control parameters * Population size * Max. no. of generations * Reproduction probability * Crossover probability * Max. depth allowed (initial and final) Termination of GP Usually set by maximum number of generations specified Best program created thus far usually selected as winner

GP Process 1. Initialize population of computer programs 2. Determine fitness of each program 3. Reproduce according to fitness values and reproduction probability 4. Perform crossover of subexpressions 5. Go to step 2 unless termination conditions are met Initialization Initialize with randomly-generated programs using defined functions and terminals (build rooted tree)

No restrictions apply except maximum depth (which is significant!) Population size varies, but 500 is common with Koza et al. Randomly Generated Programs Start with root function with number of branches = arity + Examples of generating population: 2 approaches: Grow method - select randomly from functions and terminals, except at maximum depth, where terminal must be selected

(Ratio of #functns:#terms is important) Full method - Each limb extends for full allowed depth. Only functions are selected until max depth is reached, then only terminals are selected. Ramped Half-and-Half Approach * Evenly distribute depth parameter from 2 to max. depth. So if max. depth is 11, 10 percent of population will have each depth (2, 3, ..., 11) * Within each depth, 1/2 of the programs are built using grow

approach, 1/2 using full approach GP Fitness Fitness must be calculated over a number of cases for each program. Average value often taken to be fitness. (Use perhaps 100 cases). Same cases usually used throughout the run. Four ways to calculate fitness defined by Koza: - Raw fitness - Standardized fitness - Adjusted fitness - Normalized fitness

GP Raw Fitness Can be calculated various ways Direct - score, profit, miles traveled (max. or min.) A frequently-used method is to sum over all cases of the absolute value of the error (sum-squared, Hamming, etc.) GP Standardized Fitness Lower values desirable (min. usually set = 0) Sometimes same as raw fitness (error minimized) If more is better, subtract raw fitness from max. fitness

Adjusted Fitness Adjusted fitness Fa = 1/(1-Fs), where Fs is standardized fitness Values range from 0 to 1, where 1 is optimum Koza uses adjusted fitness for many problems Small changes in standardized fitnesses near optimum have greater impact than those far away Provides a sort of scaling feature GP Normalized Fitness Calculated using adjusted fitnesses Calculated in similar manner to GA normalized fitnesses

GP Reproduction and Crossover Often carried out in parallel Sum of probabilities for reproduction and crossover = 100 - 10 % for reproduction typical - 90 % for crossover typical Both done after fitnesses are calculated If reproduction selected, roulette wheel selection used Over-selection of highly-fit individuals in large (<1000) populations is sometimes used Crossover

If crossover selected, pick two parents using roulette wheel selection Pick one point randomly in each parent for crossover - point can be anywhere in program Exchange crossover point roots and everything below them Entire programs (root on down) can be exchanged When resulting program after crossover would result exceed maximum depth, copy unaltered program into new population GP Final Thoughts * Preprocessing as used in other EC tools not as

important in GP But * Selection of functions and terminals is similar in concept to preprocessing * For assistance with formulating problems, see Chapter 26 in Koza (1992) Particle Swarm Optimization

Introduction and basics Comparison of GAs and PSO Advanced PSO concepts Particle Swarm Optimization Overview Developed with Dr. Jim Kennedy, Bureau of Labor Statistics, Washington, DC A concept for optimizing nonlinear functions using particle

swarm methodology Has roots in artificial life and evolutionary computation Simple in concept Easy to implement Computationally efficient Effective on a wide variety of problems Evolution of concept and paradigms Discovered through simplified social model simulation Related to bird flocking, fish schooling and swarming theory Related to evolutionary computation: genetic algorithms and

evolution strategies Kennedy developed the cornfield vector for birds seeking food Bird flock became a swarm Expanded to multidimensional search Incorporated acceleration by distance Paradigm simplified Particle Swarm Optimization Process 1. Initialize population in hyperspace 2. Evaluate fitness of individual particles

3. Modify velocities based on previous best and global (or neighborhood) best 4. Terminate on some condition 5. Go to step 2 PSO Velocity Update Equations Original global version: vid vid c1rand () pid xid c2 Rand () p gd xid xid xid vid Where d is the dimension, c1 and c2 are positive constants, rand and Rand are random functions, and w is the inertia weight.

For the neighborhood version, change pgd to pld . Basic Principles of Swarm Intelligence Proximity principle: the population should be able to carry out simple space and time computations Quality principle: the population should be able to respond to quality factors in the environment Diverse response principle: the populations should not commit its activities along excessively narrow channels Stability principle: the population should not change its mode of behavior every time the environment changes

Adaptability principle: the population must be able to change behavior mode when its worth the computational price Adherence to Swarm Intelligence Principles Proximity: n-dimensional space calculations carried out over series of time steps Quality: population responds to quality factors pbest and gbest (or lbest) Diverse response: responses allocated between pbest and gbest (or lbest)

Stability: population changes state only when gbest (or lbest) changes Adaptability: population does change state when gbest (or lbest) changes Applications and Benchmark Tests Function optimization De Jongs test set Schaffers f6 function Neural network training XOR

Fishers iris data EEG data 2500-pattern SOC test set Benchmark tests Compare gbest and lbest Vary neighborhood in lbest Schaffers F6 Function VMAX An important parameter in PSO; sometimes the only one adjusted Clamps particles velocities on each dimension

Determines fineness with which regions are searched If too high, can fly past optimal solutions If too low, can get stuck in local minima PSO Initial Version Fundamental assumptions seem to be upheld: Social sharing of information among conspeciates provides and evolutionary advantage. PSO has a memory: it is thus related to the elitist version of GAs Roles in the PSO Equation and

the Inertia Weight Without changes, particles fly at constant speed to boundary Without v term, the best particle would have 0 velocity, and other particles would statistically contract to optimum Originally, we set c1 and c2 to 2.0, then tried values between 1 and 2. A parameter that balances global and local search was introduced: an inertia weight w. PSO Equations with Inertia Weight

vid w vid c1rand () pid xid c2 Rand () p gd xid xid xid vid where w is the inertia weight. Inertia Weights and Constriction Factors in Particle Swarm Optimization: Introduction We compare the performance of particle swarm optimization using an inertia weight versus using a constriction factor Several benchmark functions are used

PSO Update Equations Using Constriction Factor Method v id K * [v id c1 * rand( ) * (p id - x id ) c 2 * Rand( ) * (p gd - x id )] K 2 2 - - 2 4 where c1 c 2 , 4 Phi was set to 4.1, so that K = 0.729; multiplier is thus 1.49445.

Benchmark Functions Parabolic function Rosenbrock function

30 dimensions, Xmax = 5.12, error < 100 Griewank function 30 dimensions, Xmax = 30, error < 100 Rastrigrin function

30 dimensions, Xmax = 100, error < 0.01 30 dimensions, Xmax = 600, error < 0.05 Schaffers f6 function 2 dimensions, Xmax = 100, error < 0.00001

Each version of each function was run 20 times for each benchmark function. IUPUI Parabolic Function Average No. of Iterations Inertia Weight 1538

Range (No. of Iter.) 130 Constriction F. Vmax=100K 552 96 Constriction F.

Vmax=Xmax 530 78 Rosenbrock Function Average No. of Iterations Inertia Weight 3517

Range (No. of Iter.) 1640 Constriction F. Vmax=100K Constriction F. Vmax=Xmax 1424

4318 669 992 Rastrigrin Function Average No. Range of Iterations (No. of Iter.) Inertia Weight 1321

961 Constriction 943* 6823 F. Vmax=100K Constriction 213 175 F. * Vmax=Xmax

Note: Target error not achieved for one run Griewank Function Average No. Range of Iterations (No. of Iter.) Inertia Weight 2901 1335 Constriction 437* 279

F. Vmax=100K Constriction 313 84 F. * Vmax=Xmax Note: Target error not achieved for three runs; error relaxed to 0.1 Schaffer f6 Function

Average No. of Iterations Range (No. of Iter.) 409 Inertia 512 Weight Constriction 431

794 F. Vmax=100K Constriction 532* 1952 F.* Note: Avg. = 453, range=803 with one outlier removed Vmax=Xmax Conclusions

Best approach is to use constriction factor, limiting the maximum velocity Vmax to the dynamic range Xmax Performance on benchmark functions is superior to any other results known to the authors Method has been incorporated into several applications

IUPUI Fuzzy Adaptive Inertia Weight: A Preview Uses a set of fuzzy rules to adapt an inertia weight First application to Rosenbrock function with asymmetric initialization Nine rules Two inputs: global best fitness and current inertia weight One output: change in inertia weight Can significantly improve system performance

Well look at fuzzy systems in Chapter 7 Current Method Best approach is to use constriction factor, limiting the maximum velocity Vmax to the dynamic range Xmax Performance on benchmark functions is superior to any other results known to the authors Method has been incorporated into several applications Tracking and Optimizing Dynamic Systems with Particle Swarms

IUPUI Outline Types of dynamic systems

Practical application requirements Previous work Experimental design Results Conclusions and future effort IUPUI Constriction Factor Version vid K *[vid c1 * rand( ) * (pid - x id ) c 2 * Rand( ) * (pgd - x id )] K

2 2 2 - - 4 where c1 c 2 , 4 ( was set to 4.1, so K = .729) IUPUI Dynamic System Types

Location of optimum value can change Optimum value can vary Number of optima can change Combinations of the above can occur In this work, we varied the location of the optimum.

IUPUI Practical Application Requirements Few practical problems are static; most are dynamic

Most time is spent re-optimizing (re-scheduling, etc.) Many systems involve machines and people These systems have inertia 10-100 seconds often available for re-optimization Eberharts Law of Sufficiency applies: If the

solution is good enough, fast enough, and cheap enough, then it is sufficient IUPUI Previous Work Testing Parabolic Function

Offset = offset + severity Severity 0.01, .1, .5 2000 evaluations per change 3 dimensions, dynamic range 50 to +50 IUPUI Previous Work: References

Angeline, P.J. (1997) Tracking extrema in dynamic environments. Proc. Evol. Programming VI, Indianapolis, IN, Berlin: Springer-Verlag, pp. 335345 Bck, T. (1998). On the behavior of evolutionary algorithms in dynamic environments. Proc. Int. Conf. on Evol. Computation, Anchorage, AK. Piscataway, NJ: IEEE Press, pp. 446-451 IUPUI Experimental Design

Two possibilities with swarm Continue on from where we were Re-initialize the swarm

Inertia weight of [0.5+(Rnd/2.0)] used 20 particles; update interval of 100 generations When change occurred: Retained the position of each particle Reset values of pbest (also of gbest) IUPUI

PSO average best over all runs Severity = 0.5 Three dimensions 10000 1000 100 10 1 0.1 0.01 0.001 0.0001

1E-05 1E-06 1E-07 1E-08 1E-09 1E-10 PSO average best over all runs Severity = 0.1 Three dimensions 10000 1000

100 Average best value over all runs 10 1 0.1 0.01 0.001 0.0001 1E-05 1E-06

1E-07 1E-08 1E-09 1E-10 1E-11 PSO average best over all runs Severity = 0.1 10 dimensions 10000 1000

Average best value over all runs 100 10 1 0.1 0.01

0.001 0.0001 0.00001 0.000001 PSO average best over all runs Severity = 0.5 10 dimensions

10000 Average best value over all runs 1000 100 10 1

0.1 0.01 0.001 0.0001 PSO average best over all runs Severity = 1.0 10 dimensions 10000

Average best value over all runs 1000 100 10 1 0.1

0.01 0.001 0.0001 Comparison of Results: Error Values Obtained in 2000 Evaluations Severity 0.1 Severity 0.5 Angeline

5x10-4 10-3 0.01-0.10 Bck 2x10-5 10-3 Eberhart & Shi

10-10 - 10-9 10-9 10-8 IUPUI Conclusions and Future Efforts Our results, including those in 10 dimensions and

with severity = 1, are promising We are applying approach to other benchmark functions, and to practical logistics applications IUPUI

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