Quick-Links for the TL;DR Crowd

About Lisp: Describes Lisp as an overview.
High Level Overview: Describes in general what is needed to program with Lisp.
All you need

• SBCL
• Emacs
• SLIME
• asdf-install

Code examples: A few rudimentary Lisp snippets.
Other Lisp Features: Features that I did not feel like explaining
Resources

About Lisp

Note: For a complete introduction, I recommend this e-book. If you only want to learn enough to get started, read on!

Interactive

Most languages take an input file and produce an executable or run a script. Lisp is interactive: all input is entered into the REPL, and the computation’s result is printed to the screen.

REPL: Read Evaluate Print Loop. A C++ coder can think of it like this:

void lisp()
{
    while(true){
        cout<<">"; // prompt
        cout<<evaluate(read())<<endl;
    }
}

If you’ve ever used Python’s command line prompt, you’ve used a REPL. A bash prompt is a REPL. So is Lisp.

Prefix

Most expressions in C-style languages are prefix, but exceptions are made for arithmetic, such as 1/(x+3). All expressions in Lisp are prefix expressions: the first argument of an S-expression (Sexp) is a function name, and the rest of the atoms of the S-expression are the arguments for the function. The following are examples of Lisp function calls:

(/ 1 (+ x 3))         ; 1/(x+3)
(format t "~A~%" x)    ; prints the value of 'x' and a newline.

All Expressions Return Values

All expressions in Lisp return values. For instance, the ‘if‘ statement in Lisp not only chooses which execution branch to follow, but returns the result of the executed expression. ‘nil‘ is the default return value of an expression, which also evaluates to false (‘t‘ is true).

In C-style languages, expressions don’t necessarily return values. The expression ‘1+3‘ will return ‘4‘, but ‘cout<<1+3‘ doesn’t return anything (They return a reference to an ostream, so this is a mistake) void functions don’t return anything; they can’t be used as a sub-expression for a larger statement of code.

Functions in Common Lisp can return multiple values. The ‘multiple-value-bind‘ macro is used to bind multiple return values to multiple variables.

High Level Overview: What You Need To Get Started

1) An implementation. All you need is an implementation that conforms to the standards. All of them have an edge case or two, but odds are it won’t affect you. If you absolutely need to switch later (for CLisp’s fast arbitrary precision arithmetic, for instance), nothing is stopping you; most libraries support all popular implementations.

2) An editor. I personally recommend Emacs with the SLIME extension. You can also use Vim and Limp if you either prefer Vim or you can’t stand Emacs. I left Vim before I started using Emacs, so I’ve never used Limp. Caveat emptor!

You don’t absolutely need to use an editor that interacts with Lisp. It’s POSSIBLE to run a Lisp program like a script, but it usually makes as much sense as opening a bag of chips with a hammer. You will want to use the REPL for most of your Lisp tasks, and this will only be bearable from within a good editor.

3) A Lisp tutorial or reference. I highly recommend “ANSI Common Lisp” by Paul Graham [see my review].

4) An installation system, like asdf-install. Installation systems automatically load Lisp projects into your current image, and are essential for working with a large project. asdf-install allows you to download libraries from cliki.net and load them into your Lisp image from within the interactive Lisp command prompt!

Details: What You Need To Get Started

SBCL

About

Steel Bank Common Lisp (SBCL) was forked from Carnagie-Mellon University Common Lisp (CMUCL) in 1999. It is named after Carnagie and Mellon’s respective industries, steel and banking.

Most Lisps can be interpreted or compiled, depending on the context. However, SBCL compiles all Lisp code it receives. Having used SBCL for 6 months or so, there’s not much of a pause even for large Lisp files. On the other hand, the compiler is very verbose, but this can be tweaked.

Why SBCL: Popular

Naturally, I can’t find good numbers on the popularity of SBCL. However, it is noteworthy that asdf-install and LIMP were initially designed to work with SBCL. This seems to be par for the course for all of the libraries I’ve used.

Why SBCL: Active Development

Development of SBCL is still roaring along, with 1,228 commits to their Sourceforge page at the time of writing, including several in the past few weeks. New releases come at the beginning of every month, and this month was no exception: version 1.0.23 was produced December 1, 2008.

Installing SBCL

Installing SBCL: Linux

SBCL can be installed like any other package:

sudo aptitude install sbcl
# Or your local variant.

Once that’s finished, it’s easy to test the installation:

jake@justalaptop:~/code/genesis$ sbcl
(+ 2 2)
*
4

Installing SBCL: Windows

There is an “experimental” Windows installation binary: [link].

I’ve ran it for brief periods and it seems to work fine, so I’m not sure what’s so experimental about it. Again, Caveat emptor!

After installation, run SBCL. You will get a command-line prompt, and type the following:

> (+ 2 2)
*
4

It works!

Emacs

You can use whatever editor that you want to write Lisp. Emacs has good advantages:

ELisp: Emacs is scripted in its own subset of Common Lisp. Called ‘elisp’, anybody who has been working with Lisp for a few months and can type “ELisp manual” into Google can customize Emacs to their taste.

Irony alert: I typed “elisp manula” into Google on my first try.

Automatic indentation: Yeah, I know, every editor in the history of the world does automatic indentation. However, Emacs’ authors primarily write Lisp code, so they paid close attention to how Emacs interacts with Lisp. I rarely see Emacs do the Wrong Thing. When it does, I’m usually writing awful code.

SLIME: The Superior Lisp Interaction Mode for Emacs. In order to effectively code in Lisp, you need an editor mode that will interact with a Lisp implementation. Typing the code directly into a command line is nice for small experiments, but is painful for anything more than 30 lines of code (for me at least.. your pain tolerance may vary).

SLIME

SLIME combines the editing power of Emacs with the interactive nature of Lisp.

If you’ve ever used Python’s command-line prompt, you’re aware that it’s painful to use for any amount of time. The editing capability is limited to that of your console, and when you exit Python, all of your code disappears!

SBCL (and all other Lisp implementations) is no different. If this were the only option for working with Lisp, nobody would. You must work with the REPL from within a real editor. You COULD write code in an external file and load it into SBCL from the command line, but that removes the interactive nature of Lisp.

Enter SLIME (Superir Lisp Interactive Mode for Emacs). SLIME spawns a new Lisp process and acts as the liaison between you and the process.

To start the whole process, just type “M-x slime“, and a new REPL buffer opens. Any Lisp command can be entered into this buffer, and you get all of the editing commands from Emacs. slime-mode begins in all of your open Lisp buffers, which gives you code completion and function argument hints for all compiled functions.

If slime-mode is enabled in a Lisp buffer, you can compile the file with “C-c C-k“, or compile a single function with “C-c C-c” (it looks to me like dependencies are propagated correctly). SLIME knows what capability your Lisp has, so it can take advantage of implementation-specific capabilities like debugging.

Installing SLIME

SLIME is installed the same way as any other Emacs plugin:

• Download and extract.
• Add the load path to your .emacs file
• Add any other code snippets required.

In this case, you’re going to be adding the following code to your .emacs file (open using “M-x M-f ~/.emacs“):

(add-to-list 'load-path "<em>/the/path/to/slime</em>")
(require 'slime)
(add-hook 'lisp-mode-hook (lambda () (slime-mode t)))
(add-hook 'inferior-lisp-mode-hook (lambda () (inferior-slime-mode t)))
(setq inferior-lisp-program "sbcl")

I haven’t tested it on Windows, so I’m not sure if there are any extra gotchas. If you find that you’re running into undefined function errors, one possible workaround is to install Cygwin and add C:\cygwin\bin and c:\cygwin\usr\bin to your %PATH% environment variable. Some people run away in horror at the idea, so if you don’t like it, you’re on your own.

asdf-install

One thing that you should research is asdf-install, a package manager for Lisp. If you find that you need a package that is found on cliki.net, you can download it with one step:

> (require 'asdf-install) ; Not the one step.
("ASDF-INSTALL")
> (asdf-install:install 'postmodern) ; The one step.
NIL

The above installs Postmodern, a simple Postgres interface for Common Lisp. Once you’ve done this, you only need to load the package into your Lisp image. This is also accomplished using asdf-install:

> (asdf:oos 'asdf:load-op :postmodern)
NIL

Using those 3 lines, you can now start to define database connections and operations on the database, without ever leaving Emacs!

Starting to Code

I highly recommend that you find a real reference or a real tutorial and start using that to write code. However, if you’re looking for things to enter into the REPL, I’ll give you some overviews of the basics of Lisp.

Arithmetic

> (+ 2 2)
4

This adds 2 and 2. Notice that Lisp arithmetic (and all Lisp functions) have prefix notation: the function name always comes first. You can nest values like the following:

> (+ (+ 1 1) 1)
3

Printing

> (format t "Hello, ~A~%" "Jacob")
"Hello, Jacob"
nil

‘format‘ is the function name. This is the Lisp-version of ‘printf‘,

The second argument is the destination stream. You can give this ‘t‘ or ‘nil‘ for true or false, or the name of a stream.

When ‘t‘ is given, the value is printed to Standard Output. When ‘nil‘ is given, the value is returned as a string.

The third argument is the formatting string. “~” is the escape character. “~A” means that it takes an argument that follows (like printf), and ~% is a newline.

There are other functions that you can use for printing, like ‘princ‘. I usually stick with ‘format‘, but there are different print semantics for different functions.

Defining functions

> (defun adding-function (arg1 arg2)
    "Adds arg1 and arg2."
    (+ arg1 arg2))
ADDING-FUNCTION
 
> (adding-function 1 2)
3
 
> (+ (adding-function 1 2) 1)
4

Here we see a function definition and two function calls. Let’s examine the function definition:

> (defun adding-function (arg1 arg2)

This can be considered the prototype line. ‘adding-function’ is the name of the function. It takes two mandatory parameters, ‘arg1′ and ‘arg2′, both with no default values.

"Adds arg1 and arg2."

This is the “documentation string.” A string may optionally be the first atom of a function, and it can be accessed from within Lisp using Lisp’s documentation reading abilities. Note that unlike wimpy strings in most languages, Lisp strings can contain newlines with no extra syntactical work.

(+ arg1 arg2))

This is the executed statement within the function body. A Lisp function returns the return value of the last statement. Since this is the only statement, it returns the addition of arg1 and arg2.

Defining variables

Defining a variable for the first time in SBCL:

(defvar my-var) ; my-var is 'nil'.
(defvar my-var 'a-value) ; my-var is 'a-value'.

Notice the quote in 'a-value. The single quotation mark indicates that the following expression is data, not code. Otherwise, it would try to look up the value of the variable a-value, which we may or may not have defined. Either way, it’s not what we want.

If a variable is already defined, you can use ‘setf‘:

> (setf my-var 3)
3

Conditionals

For statements that only execute when something is true, use ‘when‘. For statements that only execute when something is false, use ‘unless‘.

> (when (= 1 1)
    t)
t
> (unless (= 1 1)
    t)
nil

For statements of the form “if (true), do (x), otherwise do (y)“, use ‘if‘.

> (if expression
    x
    y)

If ‘expression‘ doesn’t evaluate to ‘nil‘, then ‘x‘ is executed and its return value is returned. Otherwise, ‘y‘ is executed and its return value is returned.

For statements involving a lot of if, else-if clauses in other languages, use cond:

(cond ((test1) (expression1))
      ((test2) (expression2))
      ; ...
      (t (default-expression)))

Iteration

The two easiest ways to iterate are the macros ‘dotimes‘ and ‘dolist‘.

> (dotimes (i 3)
    (format t "~A" i)
012
nil

‘dotimes‘ is exactly as it sounds: it executes a statement a number of times. You assign a variable (in our case, ‘i‘) and tell it the number of times it shall execute, and the variable takes on the values of all of the integers in the range [0, n).

> (dolist (i '(1 2 3))
    (format t "~A" i))
123
nil

‘dolist‘ iterates through a list much in the same way ‘dotimes‘ does.

Reduce

The idea of ‘reduce‘ is to iterate through a set, applying a function to each value that acts as an accumulator. For instance, we can do this with addition:

> (reduce #'+ '(1 2 3))
6

Lambdas

If you want to define your own functions for ‘reduce‘ without writing formal functions, you can do them as a ‘lambda‘. That’s just a fancy word for ‘function with no name’. It consists of the name, the argument list, and a function body to execute:

> (lambda (x) (+ x 2))

This defines an anonymous function that adds 2 to the input given. The only difference from ‘reduce‘ is that we need to provide two parameters: the accumulated value and the next value in the list.

You’re going to use this. A lot. Formally defining every function is not worth it. A lot of Lisp code ends up much cleaner with functional approaches.

To define our own addition function for ‘reduce‘:

> (reduce (lambda (x y) (+ x y)) '(1 2 3))
6

Other features of Common Lisp

• Macros: Creating code on-the-fly
• CLOS: Common Lisp Object System. Lisp’s answer to Object Oriented programming.
• Packages: This is how Lisp does namespacing.
• More iteration constructs than you know what to do with.
• ‘mapcar‘: Apply a function to each element of a list
• Hash tables
• Arbitrary precision integers and numbers
• cons: “Construct” function. This is the function that builds lists.car: Returns the first element of a list.
• cdr: Returns the tail of a list (everything but the car).
• Arbitrary binary manipulation
• ‘eval‘: Generate code from data at runtime.
• File streams, input streams, output streams, stream redirections, oh my!
• FFI: Foreign function interface. This lets you interface Lisp with C. You’ll probably need to do this if you want to add functionality to Common Lisp. It’s actually surprisingly easy to use.

Resources

Books

ANSI Common Lisp by Paul Graham. A great first Lisp book. It can be used as a textbook.

Paradigms of Artificial Intelligence Programming by Peter Norvig. The best programming book I have ever read. It does have quite a bit on AI, but it also has extensive sections on advanced Lisp programming.

Websites

SBCL: The Lisp implementation I currently use.

SLIME: A Lisp interaction plugin for Emacs.

Limp: A SBCL plugin for Vim.

cliki.net: A general Lisp resource. The home of all projects remotely installable using asdf-install.

Common Lisp Cookbook: An incomplete, yet still helpful, recipe book for Lisp code.

I’ve been programming a lot recently (instead of blogging about programming!). I caught the genetic algorithm bug along with the rest of the internet, so I’m in the middle of writing a Lisp library to make it easier to develop genetic algorithms: Genesis [Github link].

I’ve made some progress in the past few weeks!

The first version of my program was only a genetic programming library, and barely. It produced a rule set that could be evaluated as a function, but it wasn’t anything different than hill-climbing (a phrase I learned from snobby Reddit commenters). It was inefficient, it was unwieldy, but it produced great results when it ran long enough.

I’ve added a population! An arbitrary number of critters evolve in parallel at the moment. Previously, the individual would just mutate. I also added a basic gene-sharing algorithm: random merging.

It’s faster! In my first version, I used two ‘eval‘s per rule per round. Now I use 1, and I will cut it down to 2 per rule evaluation (instead of per round) soon.

It’s in a package! I finally decided to follow my own advice and namespace everything properly. The package layout still needs work, as I’ve been too busy coding to figure out the ASDF system for installing packages.

The package system in Lisp is a lot more flexible than most module systems I’ve ever used, so I’ve been reading a few other popular Lisp packages to see how they are organized before I jump off the deep end without my swimmies.

I started to add tests! Some of the functions are very fundamental in nature, so I’ve started to add solid tests for them.

I segregated the example! There are currently 2 different files: genesis.lisp and square-root-sample.lisp. square-root-sample shows the code necessary to produce a simple/stupid example: finding an algorithm to calculate square roots. As I’ve mentioned before, the results are very good:

All you need to do in order to run this example is load genesis.lisp and square-root-sample.lisp and call the following:

(square-root-sample <em>generations population-size</em>)

After it has finished (run a small number of generations to get a sense for the time it takes), you can call the following to get the best answer so far:

(funcall-best *CURRENT-POPULATION* #'sample-fitness-function 16)

Work Needed

A recent popular example of genetic programming is the production of the Mona Lisa using random polygons. My program should almost be able to handle this. I don’t allow for a “modify rule” function at the moment, only a “new rule” function. It would probably work, but convergence would be even slower than the example given.

I’m also not allowing for one of the most powerful methods of genetic algorithm development: binary serialization. Imagine: if you can represent your whole algorithm as a binary string, then crossover, reproduction, and mutation are all made trivial. I am instead using this idea for lists, which is convenient in Lisp, but I feel it lacks some of the punch.

On The Horizon

Of course, this is still in the toy phase. I still have a pretty substantial to-do list, but here are some of the high-notes:

• Finish writing tests for core functions.
• Multiple populations, including cross-breeding between populations.
• Add multithreading.
• Give the user much greater control over (genetic-algorithm) by allowing extra key arguments.
• Self-awareness: Gather statistics on which alterations produce better improvements, and make beneficial changes more likely to occur.

What are Genetic Algorithms?

Genetic algorithms are one of the best things that computer science has produced. Rather than figuring out a good algorithm yourself, you let your random number generator find one while you sleep! Well, after an up-front design cost to generate proper random functions, and evaluating them, and determining how you’ll evolve them, and how long they should run…

I’ve long been interested in genetic algorithms, and now that I’ve learned my first language with an eval — Lisp — I’m starting to make a library. I spent the past few days prototyping and testing my code,
and I got some encouraging initial results: I got a great almost-linear approximation to my simple test problem (finding square roots) using only addition, subtraction, multiplication, and division (see below).

I know, square roots. What a scorcher. But real people are making real solutions to real problems using genetic algorithms! NASA engineers produced an antenna. They determined acceptable design parameters for the antenna, figured out how to evaluate new designs, and let randomness determine the rest.

The result is described here.

My Genetic Algorithm Plans

I want to produce a Lisp package over the next few weeks (since I should have a boatload of freetime around Christmas) that makes it easy to define new genetic algorithms. I’m going to do it in a few stages, and blog about intermediate results if and when they are interesting.

This will also give internet denizens plenty of opportunity to criticize my awful Lisp code! I am a nublet when it comes to Lisp, and I have no illusions about the quality of the code I’m writing.

This week: prototyping. I picked an easy problem to work out the basics of genetic algorithms. My problem? Finding square roots between 0 and 99, inclusive, with just one rule set.

Disclaimer: This is potentially the stupidest use of genetic programming known to man. Attempting to regularly solve numeric problems with genetic algorithms can lead to blindness, irrational exuberance towards Republican politics, and death.

To give you a sense of the data it ingests, I present the main interface:

(defun genetic-algorithm (generations starting-rules rule-fun
                          fitness-fun)
  "Runs 'generations' number of generations. The rules are initially
  set to to 'starting-rules', the rule generating function is
  'rule-fun', and the evaluation function is 'fitness-fun'"
  (prep-rules starting-rules)
  (dotimes (gen-num generations)
    (run-generation rule-fun fitness-fun)))

Here is the invocation I used for my square root problem:

(genetic-algorithm 32000 '((/ num 2) (+ num .2))
                   #'sample-rule-fun #'sample-fitness-fun)

The typical work for defining genetic algorithms is split into a few stages:

1) Problem definition: What do I even want to solve?
2) Evaluation function: How far am I from right?
3) Picking components: How can I build my algorithms randomly?

Problem Definition

Find the square root of an integer, n, in the range [0, 99].

Evaluation Function

Sum of the squared error of all of the integers in the problem statement.

Interestingly, we can predict the quality and form of the results: since we are minimizing the sum of the squared errors, the result should mimic a least-squared approach. Since I’m only using basic arithmetic, it probably won’t be a very good one (linear at best..).

Prototype Implementation

Warning: There be dragons in the parens ahead.

The rules are stored as a list of lists:

(defvar *RULES* nil)

We also store the possible operators:

(defvar *sample-nodes* #(leaf + leaf - leaf / leaf * leaf))

Woah, what’s with all of the “leaf”s? Well, a balanced binary tree has about as many nodes as leafs (I’m generalizing…), so I started at leaf/operator parity and moved up until I stopped getting stack overflows when generating new functions, which happened at one .

How do the leafs get translated into values? First, they are picked from an array of random values:

(defvar *sample-values*
    #(-1 1 num num randomnum randomfrac))
 
(defun sample-generate-leaf (list)
  "Picks a random element from 'list' and translates it into the proper atom
  or list."
  (let ((elt (random-array-element list)))
    (case elt
         (randomnum (random 1024))
         (randomfrac
          (let ((val (ignore-errors (/ (random 1024) (random 1024)))))
            (when (null val)
              (setf val (/ 1 2)))
            val))
         (t elt))))

These are pretty self descriptive (num is the input number, and it is included twice because I got better results ). ‘randomnum‘ is a random integer in [0, 1023], and ‘randomfrac‘ is a fraction whose
coefficients are in [0, 1023].

So how is all of this put together?

(defun sample-rule-fun ()
  "Example random rule generator. Generates a random arithmetical
  expression."
  (let ((elt (random-array-element *sample-nodes*)))
    (case elt
      (leaf (sample-generate-leaf *sample-values*))
      (t
       (append (list elt) (at-least-once #'sample-rule-fun))))))
 
(defun sample-generate-leaf (arr)
  "Picks a random element from 'arr' and translates it into the proper atom
  or list."
  (let ((elt (random-array-element arr)))
    (case elt
    (randomnum (random 1024))
    (randomfrac
     (let ((val (ignore-errors (/ (random 1024) (random 1024)))))
       (when (null val)
         (setf val (/ 1 2))) ;; avoid divide by zero
       val))
    (t elt))))

This is also fairly straightforward: if you pick an operator, generate a new list, if you pick a leaf, generate a new value.

Results

Square Root Function Test

I ran it for about 18 hours (overnight and after work), and when I came back, the results were nothing short of amazing:

I included a range of values outside of the initial zone because the results are better than I could have hoped for.

I saved the data, and then was trying to save the rules (there were 18 in all), and then I lost them from my first Emacs freeze. C’est la vie. However, my initial guess that the data would fit as a least-squares appears to be a better guess than I ever could have hoped. I want to add arbitrary polynomials, and I hope to see a higher-order best-squares fit of the data plot, with much less error.

Implementation Time

The first implementation was done in a 4-hour chunk of one evening (during which I played X-Moto for at least an hour…), which I find impressive. I probably would have spent at least 3 times that on the prototype in C++. This problem is made extremely because of several of Lisp’s features:

eval: My method is based on runtime list generation, and eval-ing it as a lambda.

Insanely simple Lisp syntax: The same syntax that defines lists also defines their programs? This reduces random function generation to a random list generation problem, which is almost trivial.

REPL: Lisp is a REPL (read-eval-print loop) language, meaning that it has an interactive prompt. Combining this with Emacs’ multiple buffer capabilities, and it’s super easy to make changes and reload.

TODO

It’s never finished!

Rules

Allow more than one rule set, define the maximum number of rules per set, define rules as arrays for better lookup, etc.

Better use of eval

I eval the rules every time I need them, as opposed to the once I should generate them. I will probably see a significant speedup when I do this.

Define a package

Namespacing everything has obvious benefits.

Cross-breed rules

Allow true genetic properties by copying subsets of rules into other rules.

Better rule trimming

My current program produces some stupid rules: such as f(x)=x. In fact, my “amazing results” had 4 rules that evaluated a number to itself out of a total if 18 rules.

Allowing the user to define rule inputs.

Putting the package in an easy-to-use location

Stay tuned for more!