Results 1 
3 of
3
Functional Genetic Programming with Combinators
"... Abstract. Prior program representations for genetic programming that incorporated features of modern programming languages solved harder problems than earlier representations, but required more complex genetic operators. We develop the idea of using combinator expressions as a program representation ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
Abstract. Prior program representations for genetic programming that incorporated features of modern programming languages solved harder problems than earlier representations, but required more complex genetic operators. We develop the idea of using combinator expressions as a program representation for genetic programming. This representation makes it possible to evolve programs with a variety of programming language constructs using simple genetic operators. We investigate the effort required to evolve combinatorexpression solutions to several problems: linear regression, even parity on N inputs, and implementation of the stack and queue data structures. Genetic programming with combinator expressions compares favorably to prior approaches, namely the works
Automated SelfAssembly Programming Paradigm: Initial Investigations
 In Proceedings of the Third IEEE International Workshop on Engineering of Autonomic & Autonomous Systems
, 2006
"... This paper presents a model that simulates a selfassembly process for software components. Initial investigations on the Automated SelfAssembly Programming Paradigm (ASAP ) is presented whereby software components are treated as a gas' molecules and their interactions, within a confined area wi ..."
Abstract
 Add to MetaCart
This paper presents a model that simulates a selfassembly process for software components. Initial investigations on the Automated SelfAssembly Programming Paradigm (ASAP ) is presented whereby software components are treated as a gas' molecules and their interactions, within a confined area with specific temperature and pressure constraints, give rise to a variety of program architectures.
Evolving Fixedparameter Tractable Algorithms Stefan A. van der Meer a Iris van Rooij a,b Ida SprinkhuizenKuyper a,b
"... One effective means of computing NPhard problems is provided by fixedparameter tractable (fpt) algorithms. An fptalgorithm is an algorithm whose running time is polynomial in the input size and superpolynomial only as a function of an input parameter. Provided that the parameter is small enough, ..."
Abstract
 Add to MetaCart
One effective means of computing NPhard problems is provided by fixedparameter tractable (fpt) algorithms. An fptalgorithm is an algorithm whose running time is polynomial in the input size and superpolynomial only as a function of an input parameter. Provided that the parameter is small enough, an fptalgorithm runs fast even for large inputs. In this paper, we report on an investigation of the evolvability of fptalgorithms via Genetic Programming (GP). The problem used in this investigation is the NPhard 2DEuclidean Traveling Salesman Problem (TSP), which is known to be fpt if the number of points not on the convex hull is taken as the parameter. The algorithm evolved in our GP study turns out to have clear characteristics of an fptalgorithm. The results suggest GP can be utilized for generating fptalgorithms for NPhard problems in general, as well as for discovering input parameters that could be used to develop fptalgorithms. 1