Linear Genetic Programming
Description
Linear Genetic Programming presents a variant of genetic programming (GP) that evolves imperative computer programs as linear sequences of instructions, in contrast to the more traditional functional expressions or syntax trees. Primary characteristics of linear program structure are exploited to achieve acceleration of both execution time and evolutionary progress. Online analysis and optimization of program code lead to more efficient techniques and contribute to a better understanding of the method and its parameters. In particular, the reduction of structural variation step size and non-effective variations play a key role in finding higher quality and less complex solutions. Typical GP phenomena, such as non-effective code, neutral variations, and code growth are investigated from the perspective of linear GP.
This book serves as a reference for researchers; it also contains sufficient introductory material for students and those who are new to the field.
Reviews / Votes
From the reviews:
"This is a very good book. It provides a well organized description of linear genetic programming (LGP). Much material, previously only available in research papers, has been consolidated, reorganized and extended for this book. . This book is primarily for the evolutionary computing researcher . . Post graduate students in genetic programming should read this book . . The book should be in university libraries. Considering the cost of many books these days this one is very well priced for its size and content." (Vic Ciesielski, Genetic Programming and Evolvable Machines, Vol. 9, 2008)
"This book addresses a subfield of genetic programming, where solutions are represented by a sequence of instructions in an imperative programming language, such as C. Genetic programming is an iterative search algorithm based loosely on the concepts of biological evolution. . Brameier and Banzhaf present a thorough overview that will serve as an excellent resource for graduate students, academics, and practitioners, who choose to work with linear genetic programming." (Steven Gustafson, ACM Computing Reviews, Vol. 49 (8), August, 2008)
More details
Other editions
Additional editions
Persons
Markus Brameier received a PhD degree in Computer Science from the Department of Computer Science at University of Dortmund, Germany,in 2004. From 2003 to 2004 he was a postdoctoral fellow at the Stockholm Bioinformatics Center (SBC), a collaboration between Stockholm University, the Royal Institute of Technology, and Karolinska Institute, in Sweden. Currently he is Assistant Professor at the Bioinformatics Research Center (BiRC) of the University of Aarhus in Denmark. His primary research interests are in bioinformatics and genetic programming.
Wolfgang Banzhaf is a professor of Computer Science at the Department of Computer Science of Memorial University of Newfoundland, Canada, and head of the department since 2003. Prior to that, he served for 10 years as Associate Professor for Applied Computer Science in the Department of Computer Science at University of Dortmund, Germany. From 1989 to 1993 he was a researcher with Mitsubishi Electric Corp.,first in MELCO's Central Research Lab in Japan, then in the United States at Mitsubishi Electric Research Labs Inc., Cambridge, MA. Between 1985 and 1989 he was a postdoc in the Department of Physics, University of Stuttgart, Germany. He holds a PhD in Physics from the University of Karlruhe in Germany. His research interests are in the field of artificial evolution and self-organization studies. He has recently become more involved with bioinformatics.
Content
2.1.5 Iteration Concepts (p.22)
Iteration of code by loops plays a rather unimportant role in genetic programming. Most GP applications that require loops involve control problems with the combination of primitive actions of an agent being the object of evolution. Data flow is usually not necessary in such programs.
Instead, each instruction performs actions with side effects on the problem environment and .tness is derived from a reinforcement signal. For the problem classes we focus on here, supervised classiffcation and approximation, iteration is of minor importance. That is not to say that a reuse of code by iterations could not result in more compact and elegant solutions.
In functional programming the concept of loops is unknown. The implicit iteration concept in functional programs denotes recursions which are, however, hard to control in tree-based genetic programming [142]. Otherwise, iterated evaluations of a subtree can have an effect only if functions produce side effects. In linear GP, assignments represent an implicit side effect on memory locations as part of the imperative representation. Nevertheless, the iteration of an instruction segment may only be effective if it includes at least one effective instruction and if at least one register acts as both destination register and source register in the same or a combination of (effective) instructions, e.g., r0 := r0 + 1.
In the following, possible iteration concepts for linear GP will be presented. These comprise conditional loops and loops with a limited number of iterations. One form of iteration in linear programs is a conditional backward jump corresponding to a while loop in C. The problem with this concept is that infinite loops can be easily formed by conditions that are always fulfilled.
In general, it is not possible to detect all in.nite loops in programs, due to the halting problem [36]. A solution to remedy this situation is to terminate a genetic program after a maximal number of instructions. The result of the program would then, however, depend on the execution time allowed.
The more recommended option is a loop concept that limits the number of iterations in each loop. This requires an additional control flow parameter which may either be constant or be varied within loop instructions. Such a construct is usually expressed by a for loop in C. Because only overlapping loops (not nested loops) need to be avoided, an appropriate choice to limit the size of loop blocks may be the coevolution of endfor instructions.
Analogous to the interpretation of branches in Section 2.1.4, a for instruction and a succeeding endfor de.ne a loop block provided that only closed loops lie in between. All other loop instructions are not interpreted.
2.1.6 Modularization Concepts
For certain problems modularization may be advantageous in GP. By using subroutines repeatedly within programs, solutions may become more compact and the same limited program space can be used more efficiently. A problem may also be decomposed into simpler subproblems that can be solved more efficiently in local submodules. In this case, a combination of subsolutions may result in a simpler and better overall solution.
