Genetic Programming: Theory, Implementation, and the Evolution of Unconstrained Solutions

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@MastersThesis{Robinson:2001:GPtieus,
  author =       "Alan Robinson",
  title =        "Genetic Programming: Theory, Implementation, and the
                 Evolution of Unconstrained Solutions",
  school =       "Hampshire College",
  year =         "2001",
  type =         "Division III thesis",
  month =        may,
  keywords =     "genetic algorithms, genetic programming, PushGP, LJGP
                 Linear Java GP, Lawnmower problem, Grazer problem",
  URL =          "http://hampshire.edu/lspector/robinson-div3.pdf",
  URL =          "http://citeseer.ist.psu.edu/498673.html",
  size =         "127 pages",
  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",
}

Genetic Programming entries for Alan Robinson

Citations