Note that the time spent by a GA in finding an optimal solution is not guaranteed to be any better than the speed of an approximation algorithm, nor even the speed of the naive approach, when solving TT (or any other NP-complete problem).
The GA approach mentioned above had been used successfully in practice, though.
The basics? Here are a few I still use to today. They're certainly not a total covering of CS, or Computing for that matter, but they cover some important basic CS.
Theory:
* "Languages and Machines: An Introduction to the Theory of Computer Science", Sudkamp, T. A.
Introduces the basic concepts of languages and computing, relating the two using formal languages and state machines. Complete and clear.
* "Computers and Intractability: A Guide to the Theory of NP-Completeness", Garey, M. R., Johnson, D. S.
This one is a classic. Covers the NP(-Complete,-Hard,...) theories, problems ("what is a problem", examples), much more.
Architecture:
* "Structured Computer Organization", Tannenbaum, A. S.
Covers the design of a common-day (err 1980-1990) computer from the ground up.
Might be a bit dated, but still good stuff.
Mathematics:
* "Discrete Mathematical Structures for Computer Science", Prather, R. E.
A must for understanding logic and proofs.
Logic:
* "Digital Logic and State Machine Design", Comer, D. J.
Want to design, implement and test computer hardware? Please read this; Good stuff.
Slashdot Mirror
Most people who have spoken up so far seem right:
GAs can be used to find sub-optimal or optimal (given enough time) solutions for NP-complete problems, Timetable (TT) being one of them.
However, I think the core benefits of using such a heuristic, in this case, might be:
1. At any point in time, you can tell the GA to stop, and you can get a feasible (but probably sub-optimal) soltuion from it,
2. GAs are, in some small ways, parallelizable.
Of couse, you're probably not looking for a whiz-bang super-duper fast utility, so (2) probably isn't useful.
Perhaps you can find some use of these:
- A presentation I did on why TT is NP-Complete,
- I spoke about a GA approach for solving the TT.
Note that the time spent by a GA in finding an optimal solution is not guaranteed to be any better than the speed of an approximation algorithm, nor even the speed of the naive approach, when solving TT (or any other NP-complete problem).
The GA approach mentioned above had been used successfully in practice, though.
The basics? Here are a few I still use to today. They're certainly not a total covering of CS, or Computing for that matter, but they cover some important basic CS.
Theory:
* "Languages and Machines: An Introduction to the Theory of Computer Science", Sudkamp, T. A.
Introduces the basic concepts of languages and computing, relating the two using formal languages and state machines. Complete and clear.
* "Computers and Intractability: A Guide to the Theory of NP-Completeness", Garey, M. R., Johnson, D. S.
This one is a classic. Covers the NP(-Complete,-Hard,...) theories, problems ("what is a problem", examples), much more.
Architecture:
* "Structured Computer Organization", Tannenbaum, A. S.
Covers the design of a common-day (err 1980-1990) computer from the ground up.
Might be a bit dated, but still good stuff.
Mathematics:
* "Discrete Mathematical Structures for Computer Science", Prather, R. E.
A must for understanding logic and proofs.
Logic:
* "Digital Logic and State Machine Design", Comer, D. J.
Want to design, implement and test computer hardware? Please read this; Good stuff.