1. Genetic Programming Lab For Chess Hadar Rottenberg & Nizan Freedman

2. The Objective Creating a chess player through an evolutionary process.

3. The Method Creating a GUI-based lab for running Genetic programming experiments that will be scalable and comprehensive.

4. What Is GP A Set of trees (tree = individual) A Set of functions (tree nodes) A set of terminals (tree leafs) Given a problem insert problem as parameter to the tree, compute tree, root node gives the final result. Perform random “evolutionary” operations on trees Combine parts of two tree into one(breeding) Mutate a branch of a tree (mutate) Keep tree for next generation (cross over)

5. Abbreviations Individual: A single chess player, represented by an evaluation tree. A tree is composed of Terminals and Functions. Is my King threatened? Is it possible to reach mate in two moves? And Terminal Function

6. Abbreviations Run: A Genetic experiment with specific Parameters. Each run has several Terminals and Functions as its building blocks. The initial Individuals Population is random. The individuals compete each other and get rankings. After each Generation end (Single competitions or Tournament) the Population is changed through Breeding\Mutation\Cross-over.

7. THE GP GUI The Application is Swing based. It allows the user to: Create a GP Experiment (run), with specific Parameters per Run. Run an Experiment and save Individuals from it. Get Graph-Statistics of Individuals fitness during run. Play Visual Chess games of two Individuals or Individual VS Crafty. Manage all Runs and Individuals created.

8. The GP Engine Requirements: Easy to use Generic Saving & Loading of experiments Evaluating individuals outside an experiment(vs. crafty) Transferring individuals between experiments and using individuals as building blocks(EPOCH Run). Modifying experiments after they started.

9. Easy to use & Generic Complete integration of parameters via the GUI. All saving and loading is done via the GUI. Automatic detection of new building blocks/problem classes (Scalability). It is left up to the user to cast the evaluated object into the right class in his building blocks classes/problem. Exception:GUI is specific to chess engine. Runs both on Linux & windows.

10. Saving Loading & Crafty Experiment parameters are saved to a file. Individuals Code is saved to same file in a LISP notation. A JAVACC interpreter was written to convert individual code into a java tree object. Each Run's individuals can be exported via the GUI to a separate file, the code generated can then be used to create an individual and used to play against crafty. A crafty class was written to be able to play against crafty both outside a run, and inside a run(evaluate individuals against crafty)

11. Transferring Individuals & Modifying On The Fly Using the javacc parser we can dynamically create individuals from code written to file. All building blocks are pre-loaded dynamically to make sure we have no conflicts when importing individuals from other Runs. A Special class template for individuals to be used as building blocks, individual code is added to the template file, and compiled on the fly. The template wraps individuals trees as building blocks. These template based individuals can then be also used in other runs as building blocks. Run building blocks and most parameters can be changed after a run is saved and stopped and next time the run is started changes will take effect, allowing maximum flexibility when running experiments

12. Used Technologies Swing GUI Chess Engine Implementing Javacc Parser GP Engine with Epoch runs and ADF JFreeChart Graphs – for statistics Multi-Thread running Stream communication (with Crafty) File Handling – Reflection and Logging.