The Power and Versatility of Genetic Programming III: Darwinian Invention And Problem Solving
Genetic Programming III: Darwinian Invention And Problem Solving (Vol 3).pdf
Genetic programming is a method for enabling a computer to solve a problem by telling it what needs to be done instead of how to do it. This method borrows from the theory and techniques of biological evolution to automatically create computer programs to solve problems. In this article, we will review the book "Genetic Programming III: Darwinian Invention And Problem Solving" by John R. Koza, Forrest H. Bennett III, David Andre and Martin A. Keane, which presents their latest and most important genetically evolved solutions to dozens of problems of design, optimal control, classification, systems identification, function learning, and computational molecular biology. We will also discuss how this book advances the field of genetic programming and evolutionary computation by introducing new concepts and methods.
Genetic Programming III: Darwinian Invention And Problem Solving (Vol 3).pdf
What is genetic programming and why is it important?
Genetic programming is a branch of evolutionary computation that aims to automatically generate computer programs that can perform a desired task or function. Unlike traditional programming, where a human programmer writes a specific code for a specific problem, genetic programming uses a population of randomly generated programs that are evaluated and modified according to their fitness for the problem. The process of evaluation, selection, crossover and mutation mimics the natural evolution of living organisms, where the fittest individuals survive and reproduce, while the less fit ones are eliminated. Genetic programming can be seen as a form of artificial intelligence that can discover novel and creative solutions that may not be obvious or accessible to human programmers.
Genetic programming is important because it can solve complex and open-ended problems that are difficult or impossible to solve by conventional methods. Genetic programming can also handle problems that are dynamic, noisy or incomplete, where the optimal solution may change over time or depend on uncertain factors. Genetic programming can also generate programs that are concise, elegant and generalizable, which can be reused or adapted for other problems. Moreover, genetic programming can provide insights into the underlying structure and logic of the problem domain, by revealing the patterns, rules and principles that govern the behavior of the system.
The concept and principles of genetic programming
The basic concept of genetic programming is to represent a computer program as a tree structure, where each node corresponds to an operator or a variable, and each branch corresponds to an argument or a value. For example, the following tree represents a program that calculates the area of a circle given its radius:
(+ (* PI (* r r)) 0)
The tree representation allows for easy manipulation and modification of programs by applying genetic operators such as crossover and mutation. Crossover is a process where two parent programs exchange some parts of their trees to produce two offspring programs. Mutation is a process where a random change is made to a part of a program's tree. For example, applying crossover and mutation to the above program could result in the following offspring programs:
(+ (* PI (* r r)) (* 2 r)) // crossover (+ (* PI (* r r)) 1) // mutation
The principles of genetic programming are based on the idea of natural selection and survival of the fittest. A population of randomly generated programs is initialized and evaluated according to a fitness function that measures how well they perform the desired task or function. The fitness function can be based on the output, the error, the complexity or any other criteria that are relevant to the problem. The programs with higher fitness are selected and reproduced with some variation, while the programs with lower fitness are discarded. This process is repeated for a number of generations, until a satisfactory solution is found or a termination criterion is met.
The benefits and challenges of genetic programming
Genetic programming has many benefits that make it a powerful and versatile method for problem solving. Some of the benefits are:
It can handle problems that are complex, nonlinear, multimodal, noisy or incomplete, where conventional methods may fail or require extensive human expertise.
It can discover novel and creative solutions that may not be obvious or accessible to human programmers, by exploring a large and diverse search space of possible programs.
It can generate programs that are concise, elegant and generalizable, which can be reused or adapted for other problems, by exploiting the principles of hierarchy, modularity and reuse.
It can provide insights into the underlying structure and logic of the problem domain, by revealing the patterns, rules and principles that govern the behavior of the system.
However, genetic programming also has some challenges that limit its applicability and efficiency. Some of the challenges are:
It requires a suitable representation and initialization of programs, which can affect the quality and diversity of the initial population and the subsequent evolution.
It requires a suitable fitness function and selection mechanism, which can affect the convergence and diversity of the population and the trade-off between exploration and exploitation.
It requires a suitable termination criterion and evaluation method, which can affect the stopping condition and the validation of the final solution.
It suffers from some common issues such as bloat, overfitting, deception and premature convergence, which can degrade the performance and robustness of the method.
What are the main contributions of the book?
The book "Genetic Programming III: Darwinian Invention And Problem Solving" by John R. Koza, Forrest H. Bennett III, David Andre and Martin A. Keane is a comprehensive and authoritative work that presents their latest and most important genetically evolved solutions to dozens of problems of design, optimal control, classification, systems identification, function learning, and computational molecular biology. The book also introduces new concepts and methods that extend and improve the capabilities of genetic programming. The main contributions of the book are:
The architecture-altering operations and the genetic programming problem solver
The book introduces a new set of genetic operators called architecture-altering operations, which allow for the creation and modification of subroutines within a program. These operations include creation, deletion, duplication and alteration of subroutines, as well as calling, referencing and passing arguments to subroutines. These operations enable genetic programming to automatically discover reusable modules of code that can be invoked multiple times within a program or across different programs. These modules can improve the efficiency, modularity and generality of programs.
The book also introduces a new system called the genetic programming problem solver (GPPS), which incorporates the architecture-altering operations as well as other features such as automatically defined iterations, automatically defined loops, memory mechanisms, typing mechanisms and parallelization mechanisms. The GPPS is a general-purpose system that can handle a wide range of problems with minimal human intervention. The GPPS can automatically determine the number, type and size of subroutines, as well as the number, type and size of arguments for each subroutine. The GPPS can also automatically determine the number, type and size of memory locations for storing intermediate results or constants. The GPPS can also automatically determine whether to use iteration or recursion for repeating computations. The GPPS can also exploit parallel processing to speed up the evaluation and evolution of programs.
The genetically evolved solutions to dozens of problems
The book presents genetically evolved solutions to dozens of problems from various domains such as analogue circuit synthesis, optimal control, classification, system identification, function learning, computational molecular biology and cellular automata. These solutions demonstrate the power and versatility of genetic programming in solving complex and open-ended problems that are difficult or impossible to solve by conventional methods. Among these solutions are 14 results that are competitive with human-produced results, including 10 rediscoveries of previously patented inventions. Some examples of these solutions are: 71b2f0854b