%0 Thesis %1 Robinson:2001:GPtieus %A Robinson, Alan %D 2001 %K GP, Grazer Java LJGP Lawnmower Linear PushGP, algorithms, genetic problem problem, programming, %T Genetic Programming: Theory, Implementation, and the Evolution of Unconstrained Solutions %U http://citeseer.ist.psu.edu/498673.html %X Part I: Background 1 INTRODUCTION 1.1 BACKGROUND ? AUTOMATIC PROGRAMMING 1.2 THIS PROJECT 1.3 SUMMARY OF CHAPTERS 2 GENETIC PROGRAMMING REVIEW Part II: PushGP 3 THE PUSH LANGUAGE & PUSHGP 4 PUSHGP COMPARED TO GP2 WITH ADFS 4.1 CAN A MORE FLEXIBLE SYSTEM PERFORM AS WELL? 4.2 THE COMPUTATIONAL EFFORT METRIC 4.3 MEASURING MODULARITY 4.4 SOLVING SYMBOLIC REGRESSION 4.5 EVEN PARITY AS A GP BENCHMARK 4.6 SOLVING EVEN-FOUR-PARITY USING PUSHGP AND STACK INPUT 4.7 EVEN-FOUR-PARITY WITH INPUT FUNCTIONS 4.8 EVEN-SIX-PARITY 4.9 SOLVING EVEN-N-PARITY 4.10 CONCLUSIONS DRAWN FROM THIS CHAPTER 5 VARIATIONS IN GENETIC OPERATORS 5.1 PERFORMANCE OF BASE PUSHGP OPERATORS 5.2 VARIATIONS IN CROSSOVER 5.3 VARIATIONS IN MUTATION 5.4 EMPIRICAL TESTS WITH NEW OPERATORS 5.5 CONCLUSIONS DRAWN FROM THESE RUNS 6 NEWGROUND ? EVOLVING FACTORIAL Part III: LJGP 7 LINEAR CODED GENETIC PROGRAMMING IN JAVA 7.4 DISTRIBUTED PROCESSING 8 LJGP USER?S GUIDE 8.1 ENCODING A PROBLEM 8.2 LJGP PACKAGES AND CLASSES OVERVIEW 8.3 VCPU PROGRAMS 9 LJGP APPLIED 9.1 LAWNMOWER PILOT STUDY 9.2 PROBLEM DESCRIPTION 9.3 THE GENETIC MAKEUP OF AN INDIVIDUAL 9.4 THE MECHANICS OF EVOLUTION 9.5 PILOT RUNS OF THE LAWNMOWER PROBLEM 9.6 GRAZER PILOT STUDY 9.7 CONCLUSION TO LJGP APPLIED Conclusion APPENDIX A. COMPUTATIONAL EFFORT ? LISP CODE APPENDIX B. GENETIC PROGRAMMING SYSTEMS IN JAVA APPENDIX C. LJGP/JAVA-VM BENCHMARKS

@mastersthesis{Robinson:2001:GPtieus, abstract = {Part I: Background 1 INTRODUCTION 1.1 BACKGROUND ? AUTOMATIC PROGRAMMING 1.2 THIS PROJECT 1.3 SUMMARY OF CHAPTERS 2 GENETIC PROGRAMMING REVIEW Part II: PushGP 3 THE PUSH LANGUAGE & PUSHGP 4 PUSHGP COMPARED TO GP2 WITH ADFS 4.1 CAN A MORE FLEXIBLE SYSTEM PERFORM AS WELL? 4.2 THE COMPUTATIONAL EFFORT METRIC 4.3 MEASURING MODULARITY 4.4 SOLVING SYMBOLIC REGRESSION 4.5 EVEN PARITY AS A GP BENCHMARK 4.6 SOLVING EVEN-FOUR-PARITY USING PUSHGP AND STACK INPUT 4.7 EVEN-FOUR-PARITY WITH INPUT FUNCTIONS 4.8 EVEN-SIX-PARITY 4.9 SOLVING EVEN-N-PARITY 4.10 CONCLUSIONS DRAWN FROM THIS CHAPTER 5 VARIATIONS IN GENETIC OPERATORS 5.1 PERFORMANCE OF BASE PUSHGP OPERATORS 5.2 VARIATIONS IN CROSSOVER 5.3 VARIATIONS IN MUTATION 5.4 EMPIRICAL TESTS WITH NEW OPERATORS 5.5 CONCLUSIONS DRAWN FROM THESE RUNS 6 NEWGROUND ? EVOLVING FACTORIAL Part III: LJGP 7 LINEAR CODED GENETIC PROGRAMMING IN JAVA 7.4 DISTRIBUTED PROCESSING 8 LJGP USER?S GUIDE 8.1 ENCODING A PROBLEM 8.2 LJGP PACKAGES AND CLASSES OVERVIEW 8.3 VCPU PROGRAMS 9 LJGP APPLIED 9.1 LAWNMOWER PILOT STUDY 9.2 PROBLEM DESCRIPTION 9.3 THE GENETIC MAKEUP OF AN INDIVIDUAL 9.4 THE MECHANICS OF EVOLUTION 9.5 PILOT RUNS OF THE LAWNMOWER PROBLEM 9.6 GRAZER PILOT STUDY 9.7 CONCLUSION TO LJGP APPLIED Conclusion APPENDIX A. COMPUTATIONAL EFFORT ? LISP CODE APPENDIX B. GENETIC PROGRAMMING SYSTEMS IN JAVA APPENDIX C. LJGP/JAVA-VM BENCHMARKS}, added-at = {2008-06-19T17:46:40.000+0200}, author = {Robinson, Alan}, biburl = {https://www.bibsonomy.org/bibtex/2ce98045ba4cda0b182f3da907a11c5e4/brazovayeye}, interhash = {e95ef3c460079f574a1f2c08f4170e0d}, intrahash = {ce98045ba4cda0b182f3da907a11c5e4}, keywords = {GP, Grazer Java LJGP Lawnmower Linear PushGP, algorithms, genetic problem problem, programming,}, month = May, school = {Hampshire College}, size = {127 pages}, timestamp = {2008-06-19T17:50:19.000+0200}, title = {Genetic Programming: Theory, Implementation, and the Evolution of Unconstrained Solutions}, type = {Division III thesis}, url = {http://citeseer.ist.psu.edu/498673.html}, year = 2001 }