Database-driven Evolutionary Computation: SQL as a full programming language

1. Database-driven Evolutionary Computation:SQL as a full programming language Brian Blaha

2. The Cost of Programming • Years ago, hardware cost much more than programmers • Today, the situation is by-and-large reversed

3. Programming Languages • Assembly directly represents a program • C abstracts low-level instructions • C++ abstracts high-level operations • Matlab abstracts matrices, neural networks, and other constructs • Lisp abstracts memory management • SQL abstracts logical relationships

4. Programming Efficiency • Assembly is rock bottom • C allows portability across platforms, but maintaining old code can be difficult • C++ provides common data structures but legacy elements of C make its code obscure • All of the above usually require a program, rather than instructions, be written and tested

5. Programming Efficiency • Matlab code can be written and tested at the same time, but the function libraries are complex • Lisp encourages hierarchical program design, and its code can be tested while being written • SQL provides full database functionality

6. C++ vs Lisp • Erann Gat compared the development time and ru time of comparable C++ and Lisp programs • Many programmers implemented a simple algorithm in either C++ or Lisp • The resulting development times and run times serve to compare the languages

8. Interpretation • Lisp facilitates programming by simplifying syntax and by allowing incremental testing • Less skill is required to write fast Lisp programs than is required to write fast C++ programs

9. Extension to SQL • SQL has different syntactic overhead than C++, and more than Lisp. On the other hand, one SQL statement does the work of many C++/Lisp statements • SQL allows incremental testing, especially when transactions and rollbacks are used • Rules for streamlining database operations tend to be simple

10. Unique Advantages • SQL very naturally supports observation of a currently executing algorithm • SQL allows for asynchronous alteration of an algorithm’s data • Large sets of input data are usually in databases in the first place • SQL proficiency is more common than Lisp proficiency

11. SQL vs C++ • A genetic algorithm is a complex algorithm, rather than a simple number-crunching algorithm like a Fourier transform • Steps in a generation tend to involve population-wide operations

12. C++ Version of an Individual class Individual{ private: double fitness; Gene* genes; size_t genes_count; };

13. SQL Version of an Individual create table individual ( ID serial constraint individual\_ID_pk primary key, fitness double precision, rank integer, population integer constraint individual_population_nn not null );

14. SQL vs C++ • Therefore, a simple genetic algorithm was implemented in SQL and C++ • No external data was input during the execution, as normally might be the case

15. SQL vs C++ Time per generation

16. Mitigating Factors • Lisp code can be compiled for greater execution speed • SQL code cannot be directly compiled, but C++ classes and functions exist that allow indirect compilation • Normal SQL operation involves hard disk activity which greatly slows algorithms