- author = "John R. Koza",
- title = "Integrating symbolic processing into genetic algorithms",
- booktitle = "Workshop on Integrating Symbolic and Neural Processes at AAAI-90",
- year = "1990",
- address = "Boston",
- month = "29 " # jul,
- publisher = "AAAI",
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://www.genetic-programming.com/jkpdf/aaai1990isnp.pdf",
- size = "6 pages",
-
abstract = "Although genetic algorithms, like neural networks, are
seemingly inappropriate for handling symbolically
oriented problems, recent work in the fields of both
the genetic algorithm and neural network argues
otherwise. This presentation will discuss a group of
seemingly different problems from symbolic processing,
artificial intelligence, and machine learning which can
be solved using genetic algorithms if the appropriate
representation scheme and appropriate modifications to
the repertoire of genetic operations are adopted.
The approaches used in applying genetic algorithms to such symbolic problems may shed light on the problem of applying neural networks to symbolic problems.
Many problems from symbolic processing appear to be inappropriate candidates for solution via genetic algorithms because they, in effect, require discovery of a computer program that produces some desired output value when presented with particular inputs. However, with an appropriate representation scheme and appropriate modifications of the traditional genetic operations, it is possible to genetically search the space of possible computer programs for a most fit individual computer program. In this new {"}genetic programming{"} paradigm, populations of computer programs (LISP symbolic expressions) are genetically bred using the Darwinian principle of survival of the fittest and using a genetic crossover (recombination) operator appropriate for genetically mating computer programs.
Depending on the terminology of the particular field of interest, the {"}computer program{"} may be called a robotic action plan, an optimal control strategy, a decision tree, an econometric model, a game-playing strategy, the state transition equations, the transfer function, or, perhaps merely, a composition of functions. Similarly, the {"}inputs{"} to the {"}computer program{"} may be called sensor values, state variables, independent variables, attributes of an object, or, perhaps more prosaically, the arguments to a function.
The methods for applying genetic algorithms to symbolic problems are illustrated using examples from the areas of function learning, robotic planning, symbolic function identification, symbolic regression, symbolic integration and differentiation, symbolic solution of differential equations, game-playing, sequence induction, empirical discovery and econometric modeling, concept formation, automatic programming , pattern recognition, time-optimal control. Problems of the type described above can be expeditiously solved only if the flexibility found in computer programs is available. This flexibility includes the ability to perform alternative computations conditioned on the outcome of intermediate calculations, to perform computations on variables of many different types, to perform iterations and recursions to achieve the desired result, and to define and subsequently use computed values and sub-programs.
The process of solving these problems can be reformulated as a search for a most fit individual computer program in the space of possible computer programs composed of various terms (atoms) along with standard arithmetic operations, standard programming operations, standard mathematical functions, and various functions peculiar to the given problem domain. Four types of objects are manipulated as we build computer programs, namely, functions of various number of arguments; variable atoms; constant atoms; and control structures such as If-Then-Else, Do-Until, etc.",
-
abstract = "As will be seen, the LISP S-expression required to
solve each of the problems described above will emerge
from a simulated evolutionary process. This process
will start with an initial population of randomly
generated LISP S-expressions composed of functions and
atoms appropriate to the problem domain. Then, a
process based on the Darwinian model of reproduction
and survival of the fittest and genetic recombination
will be used to create a new population of individuals.
In particular, a genetic process of sexual reproduction
among two parental S-expressions will be used to create
offspring S-expressions. At least one of two
participating parental S-expressions will be selected
in proportion to fitness. The resulting offspring
S-expressions will be composed of sub-expressions
({"}building blocks{"}) from their parents. Finally,
the new population of offspring (i.e. the new
generation) will replace the old population of parents
and the process will continue.
As will be seen, this highly parallel, locally controlled, and decentralized process will produce populations which, over a period of generations, tend to exhibit increasing average fitness in dealing with their environment, and which, in addition, can robustly (i.e. rapidly and effectively) adapt to changes in their environment.",
- notes = "Presented at the Workshop on Integrating Symbolic and Neural Processes at AAAI-90 in Boston, MA, USA, July 29, 1990.",
