BibSonomyburst/user/brazovayeye/lawnmowerBibSonomy RSS feed for /user/brazovayeye/lawnmower2012-02-16T22:47:20+01:00http://www.bibsonomy.org/bibtex/2dae12a51b2b2388fee40ac9e2c311b61/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00Cartesian acquisition, algorithms, automatically defined embedded evolution, functions, genetic hierarchical-if-and-only-if, lawnmower module problem, program programming, synthesis synthesis, <span class="authorEditorList"><a href="/author/Walker">James Alfred Walker</a>, and <a href="/author/Miller">Julian Francis Miller</a> </span><em>GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation, </em><em> 1, </em><em>page 911--918. </em><em>Seattle, Washington, USA, </em><em>ACM Press, </em>(<em>August 2006</em>)Thu Jun 19 17:46:40 CEST 2008Seattle, Washington, USA{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation8-12 July911--918Embedded cartesian genetic programming and the lawnmower and hierarchical-if-and-only-if problems12006Cartesian acquisition, algorithms, automatically defined embedded evolution, functions, genetic hierarchical-if-and-only-if, lawnmower module problem, program programming, synthesis synthesis, http://www.bibsonomy.org/bibtex/2ce98045ba4cda0b182f3da907a11c5e4/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00GP, Grazer Java LJGP Lawnmower Linear PushGP, algorithms, genetic problem problem, programming, <span class="authorEditorList"><a href="/author/Robinson">Alan Robinson</a> </span><em>Hampshire College, </em><em>Division III thesis, </em>(<em>May 2001</em>)Thu Jun 19 17:46:40 CEST 2008MayGenetic Programming: Theory, Implementation, and the Evolution of Unconstrained SolutionsDivision III thesis2001GP, Grazer Java LJGP Lawnmower Linear PushGP, algorithms, genetic problem problem, programming, 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