Strongly Formed Genetic Programming

1. Strongly Formed Genetic Programming 06/19/2013

2. Agenda • Strongly formed genetic programming (SFGP) • Where it fits • Current progress • Next steps

3. SFGP • GP for evolving programs [Castle2012] • Uses basic programming constructs to generate code for a given problem • Extends previous work in Strongly Typed Genetic Programming [Montana1995] • Nodes specify data-type constraints on inputs • Removes need for closure • No restraint on tree structure as typical grammars • Data-type constraints provide structure • No control structure found in imperative programming constructs

4. SFGP • Extends STGP by imposing extra node restrictions • Node must define: • Data-type (integer, float, etc.) • Node-type • Allowable child terminals or non-terminals • For example, Assignment node must have a first child that is both an integer and a variable • Must be satisfied throughout evolutionary process

5. SFGP Assignment • Extends STGP by imposing extra node restrictions • Node must define: • Data-type (integer, float, etc.) • Node-type • Allowable child terminals or non-terminals • For example, Assignment node must have a first child that is an integer and a variable • Must be satisfied throughout evolutionary process Integer Variable Integer Expression intvar = 5;

6. SFGP Iterator Upper Bound For Loop • Extends STGP by imposing extra node restrictions • Node must define: • Data-type (integer, float, etc.) • Node-type • Allowable child terminals or non-terminals • For example, Assignment node must have a first child that is an integer and a variable • Must be satisfied throughout evolutionary process Integer Variable Integer Expression Code Block for (inti = 0; i < 20; ++i) { Code Block }

7. SFGP Types • Top-level • Subroutine • CodeBlock • Statement • Loop • ForLoop • ForEachLoop • IfStatement • Assignment • Expression • Add • Subtract • Multiply • And • OrNot • Literal • Variable

8. Where it Fits • Tree halves • One half provides role transformations • One half provides code generation • Using SFGP, we will generate the code for the composition strategies • Advantage of SFGP is that we can output into any language • Also high level representations of nodes, as we had before • Initial approach had some roadblocks with post-processing for generating quality code • Allow algorithm to take control

9. Where it Fits WRAPPER Code Generation Role Transformations

10. Current Progress • Implementing vanilla SFGP • Creating new primitives and functionality in Puppy • Running into some issues • At 12:30am, I realized… • STGP is already a part of OpenBEAGLE proper

11. Next Steps • Migrate to OpenBEAGLE and get sample programs up and running • Customize for our needs • Migrate and merge role transformations

12. Sample Output from Puppy • Best individual at generation 50 is: • (CODEBLOCK (OR (CODEBLOCK X X (VARIABLE X)) (ASSIGNMENT X (SUBROUTINE X X))) (LITERAL (LOOP (MULTIPLY X X) (ASSIGNMENT X X))) (IFSTATEMENT X (ADD X X))) • =================== • CODEBLOCK • VARIABLE • OR • ASSIGNMENT • SUBROUTINE • CODEBLOCK • MULTIPLY • LOOP • ASSIGNMENT • LITERAL • IFSTATEMENT • ADD • ===================

13. Frameworks • SFGP – EpochX • Java Framework • http://www.epochx.org/ • OpenBEAGLE • Provides STGP support (must be extended for SFGP) • SPAMBASE example uses STGP • Stable release (3.0.3) has a run-time error • Alpha build runs properly (4.0.0 – Alpha 2) • http://code.google.com/p/beagle/downloads/list

14. New References • [Castle2012] • T. Castle and C. G. Johnson. Evolving high-level imperative program trees with strongly formed genetic programming. In Proceedings of the 15th European Conference on Genetic Programming, EuroGP2012,volume 7244 of LNCS, pages 1–12. Springer, Apr. 2012. • [Montana1995] • Montana, D.J.: Strongly typed genetic programming. Evolutionary Computation 3 (1995) 199-230