Genetic programming

Banzhaf, Wolfgang; Koza, John R.; Ryan, Conor; Spector, Lee; Jacob, Christian

doi:10.1109/5254.846288

Cited by 79 publications

(35 citation statements)

References 13 publications

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“…Other forms of Genetic Programming use the more traditional linear representation, e.g., of machine code instructions (e.g., [Friedberg, 1958] [Ray, 1992] [Banzhaf, 1993]) or Boolean 5 expressions [Forsyth, 1981]. Most relevant to the form of GP to be explored in this paper is the relatively small amount of prior work on graph-based representations.…”

Section: Evolving Graph-based Programsmentioning

confidence: 99%

On dynamical genetic programming: simple Boolean networks in learning classifier systems

Bull

2009

International Journal of Parallel, Emergent and Distributed Sys

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Abstract. Many representations have been presented to enable the effective evolution of computer programs. Turing was perhaps the first to present a general scheme by which to achieve this end.Significantly, Turing proposed a form of discrete dynamical system and yet dynamical representations remain almost unexplored within conventional genetic programming. This paper presents results from an initial investigation into using simple dynamical genetic programming representations within a Learning Classifier System. It is shown possible to evolve ensembles of dynamical Boolean function networks to solve versions of the well-known multiplexer problem. Both synchronous and asynchronous systems are considered.

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Section: Evolving Graph-based Programsmentioning

confidence: 99%

On dynamical genetic programming: simple Boolean networks in learning classifier systems

Bull

2009

International Journal of Parallel, Emergent and Distributed Sys

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“…can be found in the book by Mitchell (1997). Relatively recent general attempts include program evolvers such as ADATE (Olsson, 1995) and simpler heuristics such as Genetic Programming (GP) (Cramer, 1985;Dickmanns, Schmidhuber, & Winklhofer, 1987;Banzhaf et al, 1998). Unlike logic-based program synthesizers (Green, 1969;Waldinger & Lee, 1969;Deville & Lau, 1994), program evolvers use biology-inspired concepts of Evolutionary Computation (Rechenberg, 1971;Schwefel, 1974) or Genetic Algorithms (Holland, 1975) to evolve better and better computer programs.…”

Section: Other Work On Incremental Learningmentioning

confidence: 99%

“…Extend the initial rules of the language to accommodate the additional primitives. (Rechenberg, 1971;Schwefel, 1974), Genetic Algorithms (Holland, 1975) and variants such as Genetic Programming (Cramer, 1985;Banzhaf et al, 1998), Probabilistic Incremental Program Evolution (Salustowicz & Schmidhuber, 1997Salustowicz, Wiering, & Schmidhuber, 1998), Ant Colony Optimization (Gambardella & Dorigo, 2000;Dorigo, Di Caro, & Gambardella, 1999), etc.…”

Section: Illustrated Informal Recipe For Oops Initializationmentioning

confidence: 99%

“…For instance, we could plug in a time-consuming algorithm such as Probabilistic Incremental Program Evolution (Salustowicz & Schmidhuber, 1997Salustowicz, Wiering, & Schmidhuber, 1998). Likewise we could use variants of Evolutionary Computation (Rechenberg, 1971;Schwefel, 1974) or Genetic Algorithms (Holland, 1975) or Genetic Programming (GP) (Cramer, 1985;Banzhaf et al, 1998) as primitives. Generally speaking, whenever there is prior knowledge about promising directions in the search space, such as gradients, and about certain potentially useful incremental learning techniques, then this knowledge should be implemented in form of primitives.…”

Section: A2 Primitive Instructionsmentioning

confidence: 99%

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Optimal Ordered Problem Solver

2004

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Abstract. We introduce a general and in a certain sense time-optimal way of solving one problem after another, efficiently searching the space of programs that compute solution candidates, including those programs that organize and manage and adapt and reuse earlier acquired knowledge. The Optimal Ordered Problem Solver (OOPS) draws inspiration from Levin's Universal Search designed for single problems and universal Turing machines. It spends part of the total search time for a new problem on testing programs that exploit previous solution-computing programs in computable ways. If the new problem can be solved faster by copy-editing/invoking previous code than by solving the new problem from scratch, then OOPS will find this out. If not, then at least the previous solutions will not cause much harm. We introduce an efficient, recursive, backtracking-based way of implementing OOPS on realistic computers with limited storage. Experiments illustrate how OOPS can greatly profit from metalearning or metasearching, that is, searching for faster search procedures.Keywords: OOPS, bias-optimality, incremental optimal universal search, efficient planning and backtracking in program space, metasearching and metalearning, self-improvement

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“…Evolutionary Programming, developed by L Fogel in the 1960s [8], was devised to discover Finite State Machine descriptions. In Genetic Programming [11], the genome is (usually) a tree structure representing a program in a HLL, and the program is evolved; Linear GP [2] is used to evolve assembly language programs. Spector's "Push" is a stack-based language designed for evolutionary computation [24].…”

Section: Related Workmentioning

confidence: 99%

PLAZZMID: An Evolutionary Agent-Based Architecture Inspired by Bacteria and Bees

Stepney

Clarke

Young

Advances in Artificial Life

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Abstract. Classical evolutionary algorithms have been extremely successful at solving certain problems. But they implement a very simple model of evolutionary biology that misses out several aspects that might be exploited by more sophisticated algorithms. We have previously critiqued the traditional naïve approach to bio-inspired algorithm design, that moves straight from a simplistic description of the biology into some algorithm. Here we present a process for developing richer evolutionary algorithms abstracted from various processes of biological evolution, with a corresponding richer analogical computational structure, and indicate how that might be further abstracted.

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Genetic programming

Cited by 79 publications

References 13 publications

On dynamical genetic programming: simple Boolean networks in learning classifier systems

On dynamical genetic programming: simple Boolean networks in learning classifier systems

Optimal Ordered Problem Solver

PLAZZMID: An Evolutionary Agent-Based Architecture Inspired by Bacteria and Bees

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