Undirected Training of Run Transferable Libraries

  title={Undirected Training of Run Transferable Libraries},
  author={Maarten Keijzer and Conor Ryan and Gearoid Murphy and Mike Cattolico},
This paper investigates the robustness of Run Transferable Libraries(RTLs) on scaled problems. RTLs provide GP with a library of functions which replace the usual primitive functions provided when approaching a problem. The RTL evolves from run to run using feedback based on function usage, and has been shown to outperform GP by an order of magnitude on a variety of scalable problems. RTLs can, however, also be applied across a domain of related problems, as well as across a range of scaled… 
Parallel Problem Solving from Nature – PPSN XV
A new large-scale testbed with dimension up to 640 is introduced, implemented within the COCO benchmarking platform and used to assess the performance of several promising variants of CMA-ES and the standard limited-memory L-BFGS.
A Simple Approach to Lifetime Learning in Genetic Programming-Based Symbolic Regression
The Chameleon system is introduced to augment GP with lifetime learning by adding a simple local search that operates by tuning the internal nodes of individuals, and uses available genetic material more efficiently by exploring more actively than with standard GP.
Extending Program Synthesis Grammars for Grammar-Guided Genetic Programming
The current shortcomings of G3P are analysed and the papers contributions include an example of extending grammars for program synthesis, a fairer comparison between PushGP and G3p with a more similar function set as well as new results on problems that have not been solved with G2P and one that has not been solving with PushGP.
Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure Deduplication
Empirical tests show that the proposed deterministic symbolic regression algorithm is competitive with genetic programming in many noiseless problems while maintaining desirable properties such as simple, reliable models and reproducibility.
Improving GP generalization: a variance-based layered learning approach
A new method that improves the generalization ability of genetic programming (GP) for symbolic regression problems, named variance-based layered learning GP, which uses the variance of the output values of a function as a measure of the functional complexity.
Tag-based modularity in tree-based genetic programming
It is shown here how tag-based modules can be incorporated into a more standard tree-based genetic programming system and results obtained using the technique on problems for which other modularization techniques have been shown to be useful.
Tag-based modules in genetic programming
It is demonstrated that tag-based modules readily evolve and that this allows problem solving effort to scale well with problem size and is effective even in complex, non-uniform problem environments for which previous techniques perform poorly.
Evolving event-driven programs with SignalGP
The value of the event-driven paradigm is demonstrated using two distinct test problems (an environment coordination problem and a distributed leader election problem) by comparing SignalGP to variants that are otherwise identical, but must actively use sensors to process events or messages.
Tag-based regulation of modules in genetic programming improves context-dependent problem solving
It is found that tag-based regulation improves problem-solving performance on context-dependent problems; that is, problems where programs must adjust how they respond to current inputs based on prior inputs.
Using context blocks to implement Node-attached Modules in genetic programming
  • M. Gregor, J. Spalek
  • Computer Science
    2013 IEEE 17th International Conference on Intelligent Engineering Systems (INES)
  • 2013
The paper presents extensions to the standard version of genetic programming that implement a novel approach to modular genetic programming based on modules stored with the abstract syntax tree, but also attached to nodes that call them (Node-attached Modules with Ancestry Tracking).


Run Transferable Libraries - Learning Functional Bias in Problem Domains
It is demonstrated that a system using Run Transferable Libraries can solve a selection of standard benchmarks considerably more quickly than GP with ADFs by building knowledge about a problem.
Genetic programming 2 - automatic discovery of reusable programs
  • J. Koza
  • Computer Science
    Complex adaptive systems
  • 1994
The Evolutionary Induction of Subroutines
A genetic algorithm capable of evolving large programs is described by exploiting two new genetic operators which construct and deconstruct parameterized subroutines which help to solve the scaling problem for a class of genetic problem solving methods.
Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems
In this new "genetic programming" paradigm, populations of computer programs 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.
Evolving Modules in Genetic Programming by Subtree Encapsulation
This paper investigates the effect of encapsulating tree-based genetic programming subtrees by representing them as atoms in the terminal set, so that the subtree evaluations can be exploited as terminal data.