The authors have written a fine book here and it has and will continue to be a source of good information on the subject. What is most interesting about the approach of genetic programming is that it does not make use of the inference methods of formal logic in the search for the correct program. Correctly observing that logical thinking is insufficient for invention and creativity, the authors follow the "logic considered harmful" philosophy in their attempts to get a computer to find a creative/original solution to a problem. And most importantly, they discuss fourteen examples where genetic programming has produced results that are competitive with human-produced results. The book is almost 1200 pages long, but without reading all the examples one could cover the main points in a reasonable time frame. The reader knowing the LISP language will appreciate the discussion more.
After a brief introduction to the book in chapter 1, the authors move on to a detailed discussion of the philosophy and approaches used in genetic programming. They list the five steps that must be done before applying a genetic algorithm to a problem and give an overview of the LISP background needed to understand genetic programming. The authors emphasize that the genetic algorithm is probabilistic in nature, with the initial populations, individual selection, and genetic operation chosen at random. They give flowcharts illustrating a typical genetic algorithm and program, and then show executable programs can be automatically created. A very extensive list of references on genetic programming is given at the end of the chapter.
In the next part, the authors discuss how to eliminate the requirement that the programmer specify the architecture in advance to the program to be created. After reviewing some methods that were previously used to make the choice of architecture, the authors move on to describing a set of architecture-altering operations that give an automated method for determining the architectures of evolving programs. The discussion on automatically defined recursion is particularly interesting.
The book then shows how to use the results so far to allow problem-solving to be done using genetic programming, the first one being the rotation of automobile tires and the second being evolving a computer program with the behavior of Boolean even-parity functions. This is followed by a discussion of how to use architecture-altering operations to solve a time-optimal control problem. The most interesting part of this discussion is that it illustrates the important point that disadvantageous actions should be taken in the short term so that the long-term objective can be achieved.
In chapter 14, the ant foraging problem is used to illustrate a form of the (Minsky) multiagent problem and architecture-altering operations. This is followed by discussions on the digit recognition problem and the transmembrane segment identification problem. The authors choose the Fibonacci sequence to illustrate how recursion can be used in solving problems with genetic programming. The necessity of using internal storage is illustrated using the cart centering problem.
The authors then overview the use of the Genetic Programming Problem Solver (GPPS) for automatically creating a computer program to solve a problem. Several problems are examined using this Solver, such as symbolic regression, sorting networks, and the intertwined spirals problem.
The next part then considers the application of genetic programming to the automated synthesis of analog electrical circuits. The authors judge, rightfully, that the design process is one that will be a good judge of automated technique versus one that was done by humans, especially considering the fact that analog design is considered by many to be an "art" rather than a "science". The authors show how to import the SPICE simulation system into the genetic programming system, and discuss how validation of circuit design using this simulator would be done by the genetic programming system. After showing how a low-pass filter may be successfully designed using the genetic programming system, the authors show how with a few changes it can be used to design many different types of circuits. Interestingly, the authors cite the rediscovery by genetic programming of the elliptic filter topology of W. Cauer. Cauer arrived at his discovery via the use of elliptic functions, but the genetic program did not make use of these, but relied solely on the problem's fitness measure and natural selection!
An interesting discussion is also given of the role of crossover in genetic programming by comparing the problem of synthesizing a lowpass filter with and without using crossover. The authors conclude that the crossover operation plays a large contribution to the actual solution of the problem.
Then later, the authors show how genetic programming actually evolved a cellular automata that performs better than a succession of algorithms written by humans in the last two decades. Specifically, they show how genetic programming evolved a rule for the majority classification problem for one-dimensional two-state cellular automata that exceeds the accuracy of all known rules.
Most interestingly, the authors show how genetic programming evolved motifs for detecting the D-E-A-D box family of proteins and for detecting the manganese superoxide dismutase family.
The actual performance and implementation issues involved in genetic programming are discussed in the last two parts of the book. They discuss the computer time needed to yield the 14 instances where they claim that genetic programming has produced results that are competitive with human-produced results.
The authors wrap things up in the last chapter of the book and discuss other instances where genetic programming has succeeded in automatically producing computer programs that are competitive with human-produced results. The evidence they have in the book is impressive but there are a few areas that will be ultimate tests of this approach, the most important being the discovery of new mathematical results or algorithms. It is this area that requires the most creativity on the part of the inventor.
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