Evolutionary Computation : A Unified Approach

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A.1. EC1: A VERY SIMPLE EC SYSTEM 235 The base class for the population maintained by an EA is defined in Population.java. The base class for individuals contained in a population is defined in Individual.java, and the base class for an individual’s genome is defined in Genome.java. In EC1 there is only one type of genome supported, namely a vector of real-valued genes. The base class for problems is defined in Landscape.java, which is then specialized in RvpLscape.java to the class RvpLscape (real-valued parameter landscapes), and specialized again in Parabola.java to parabolic landscapes. The code in Parabola.java can be used as a template to easily add other more interesting landscapes. Finally, EC rand.java and Pad.java provide some convenient utility code. A.1.2 EC1 Parameters The EC1 configuration file discussed earlier is used to modify the behavior of EC1 from one run to the next. EC1 uses the Java-provided Properties class to read in the information in a configuration file. The Properties class provided the ability to read in lines of the form: property_name=property_values When retrieving information from a file, the Properties class does a keyword search on the property name, so properties can be specified in any order in the file, one line per property. The properties implemented by EC1 are: Landscape= ... PopSize= Mutation= ... SimLimit= RandSeed= Report= So, for example, a typical configuration file might be: Landscape=RVP Parabola 2 -4.0 6.0 soft - 50.0 PopSize=10 Mutation=gaussian 2.0 SimLimit=1000 RandSeed=123456789 Report=full which specifies the general class of landscape to be RVP (real-valued parameter); the particular landscape of type Parabola; the particular parabola is 2-dimensional with initial lower/upper parameter bounds of -4.0 and 6.0; the initial bounds are soft (not hard), meaning that an EA is allowed to explore outside them; and the parabola is inverted (-) with a maximum value of 50.0. In addition, a population size of 10 is to be used, and the mutation operator to be used is Gaussian with an average step size of 2.0. The simulation is to be run for 1000 births (100 generations), the pseudo-random number generator is to be initialized with the seed value 123456789, and a full report of the simulation is to be generated.
236 APPENDIX A. SOURCE CODE OVERVIEW In EC1 there are no other landscape types implemented. So, the only variation in configuration files comes from specifying different numerical values, by choosing between a simple delta mutation operator and a Gaussian one (discussed in chapter 1), and/or by choosing a different report type. A full report (the default) prints out the kind of information seen in chapter 1: after every generation global and local population statistics are printed along with a dump of the contents of the current population. Various parts of this voluminous output can be suppressed by selecting one of the alternative report types: Report=local prints only local population statistics each generation Report=global prints only global population statistics each generation Report=bsf prints only best-so-far statistics each generation Report=pop_stats prints only local population statistics in a format easily read by a plotting program. Another example configuration file is: SimLimit=1000 Mutation=delta 5.0 Landscape=RVP Parabola 3 -10.0 10.0 hard Report=bsf In this case PopSize and RandSeed are unspecified and default to 10 and 12345, respectively. Of course, the best way to sort this all out is to read the code, which is what you are expected to do! A.2 EC2: A More Interesting EC System The Java code in this directory is a generalization of the EC1 code in the directions discussed in hapter 3, namely, the introduction of the code for canonical versions of the three historically important evolutionary algorithms (EAs): <strong>Evolutionary</strong> Strategies (ESs), <strong>Evolutionary</strong> Programming (EP), and Genetic Algorithms (GAs). The design goal for EC2 was to use the object-oriented features of Java to build a simple but flexible system that allowed one to try out the canonical EAs and get a sense of how they differ from each other, and to provide some limited capabilities for changing some of the internal components of an EA to see how EA behavior is affected. In keeping with the KISS philosophy of EC1, EC2 is also implemented as a commandline system rather than a GUI, and uses the same configuration file paradigm as EC1 to add user-selectable options to EC2. In particular, a configuration file can now contain: EA=xx where xx can be one of: ev, es, ep, or ga, allowing a user to choose which EA to apply to a particular problem. This is implemented by generalizing the class EA and adding new specializations: ES EA, EP EA, and GA EA. For simplicity in chapter 3 and in the EC2 code, there is still only one class of problems (fitness landscapes): namely, real-valued parameter optimization problems. However,
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Evolutionary Computation
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c○ 2006 Massachusetts Institute o
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vi CONTENTS 4 A Unified View of Sim
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viii CONTENTS 6.5.7 Selection, Repr
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Chapter 1 Introduction The field of
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1.2. EV: A SIMPLE EVOLUTIONARY SYST
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1.2. EV: A SIMPLE EVOLUTIONARY SYST
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1.3. EV ON A SIMPLE FITNESS LANDSCA
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1.4. EV ON A MORE COMPLEX FITNESS L
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1.4. EV ON A MORE COMPLEX FITNESS L
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1.5. EVOLUTIONARY SYSTEMS AS PROBLE
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1.6. EXERCISES 21 1.6 Exercises 1.
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24 CHAPTER 2. A HISTORICAL PERSPECT
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Chapter 3 Canonical Evolutionary Al
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3.3. EVOLUTIONARY PROGRAMMING 35 55
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3.4. EVOLUTION STRATEGIES 37 55 50
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3.4. EVOLUTION STRATEGIES 39 55 50
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3.5. GENETIC ALGORITHMS 41 Define s
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3.5. GENETIC ALGORITHMS 43 3.5.2 Un
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3.5. GENETIC ALGORITHMS 45 Populati
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3.6. SUMMARY 47 3.6 Summary The can
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Chapter 5 Evolutionary Algorithms a
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5.1. SIMPLE EAS AS PARALLEL ADAPTIV
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5.2. EA-BASED OPTIMIZATION 81 them
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5.2. EA-BASED OPTIMIZATION 83 of th
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5.2. EA-BASED OPTIMIZATION 85 one o
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5.2. EA-BASED OPTIMIZATION 87 60 40
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5.2. EA-BASED OPTIMIZATION 89 Notic
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5.2. EA-BASED OPTIMIZATION 91 120 1
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5.2. EA-BASED OPTIMIZATION 93 parti
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5.2. EA-BASED OPTIMIZATION 97 5.2.2
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5.2. EA-BASED OPTIMIZATION 99 of tr
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5.2. EA-BASED OPTIMIZATION 101 700
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5.2. EA-BASED OPTIMIZATION 103 5.2.
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5.3. EA-BASED SEARCH 105 • In the
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5.4. EA-BASED MACHINE LEARNING 107
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5.5. EA-BASED AUTOMATED PROGRAMMING
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5.5. EA-BASED AUTOMATED PROGRAMMING
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5.7. SUMMARY 113 the effect of impr
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116 CHAPTER 6. EVOLUTIONARY COMPUTA
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Page 252 and 253: Bibliography Altenberg, L. (1994).
Page 254 and 255: BIBLIOGRAPHY 243 Collins, R. and D.
Page 256 and 257: BIBLIOGRAPHY 245 Frank, S. A. (1995
Page 258 and 259: BIBLIOGRAPHY 247 Jansen, T., K. De
Page 260 and 261: BIBLIOGRAPHY 249 Potter, M. A., J.
Page 262 and 263: BIBLIOGRAPHY 251 Spears, W. (2000).
Page 264 and 265: Index 1-point crossover operator, 2
Page 266 and 267: INDEX 255 graph structures, 74-76,
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